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ah well let us go
[ "Oh well, let's go.", "Now, well, let's go.", "Now well, let's go.", "Ah well, let's go.", "Oh, well, let's go." ]
['0 well let us go', 'now well let us go', 'now well let us go', 'ah well let us go', '0 well let us go']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yes sure
[ "Yes, sure.", "Yes sure.", "Yes. Sure.", "Yes, Sure.", "Yes, sure, sure." ]
['yes sure', 'yes sure', 'yes sure', 'yes sure', 'yes sure sure']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
is the is the data always clearly split up by different series
[ "Is the data always clearly split up a different series?", "Is the data always clearly split up by different series?", "Is is the data always clearly split up a different series?", "Is the data always clearly split up in different series?", "Is the data always clearly split up a different series." ]
['is the data always clearly split up a different series', 'is the data always clearly split up by different series', 'is is the data always clearly split up a different series', 'is the data always clearly split up in different series', 'is the data always clearly split up a different series']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
0 so that is good anyway then yeah
[ "So that's good anyway then, yeah.", "So that's good anyway, then, yeah.", "So that's good anyway then yeah.", "So that's good anyway, then yeah.", "So that's good anyway then, yeah?" ]
['so that is good anyway then yeah', 'so that is good anyway then yeah', 'so that is good anyway then yeah', 'so that is good anyway then yeah', 'so that is good anyway then yeah']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i think you can do it on line
[ "I think you can do it online.", "I think you can do it on line.", "I think you can do it online?", "I think you can do it online,", "I think you can do online." ]
['i think you can do it online', 'i think you can do it on line', 'i think you can do it online', 'i think you can do it online', 'i think you can do online']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah i i do not know about the search functionality that might be online
[ "Yeah, I don't know about the search functionality that might be online.", "Yeah, I don't know about the search functionality. That might be online.", "Yeah I don't know about the search functionality that might be online.", "Yeah, I don't know about the search functionality, that might be online.", "Yeah. I don't know about the search functionality that might be online." ]
['yeah i do not know about the search functionality that might be online', 'yeah i do not know about the search functionality that might be online', 'yeah i do not know about the search functionality that might be online', 'yeah i do not know about the search functionality that might be online', 'yeah i do not know about the search functionality that might be online']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
then it should not ever fail because then it should never
[ "Then it should never fail, because then it should never", "Then it should never fail, because it should never", "then it should never fail, because then it should never", "Then it should never fail because then it should never", "Then it shouldn't ever fail, because then it should never" ]
['then it should never fail because then it should never', 'then it should never fail because it should never', 'then it should never fail because then it should never', 'then it should never fail because then it should never', 'then it should not ever fail because then it should never']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
but
[ "Thank you.", "That's right.", "Thank you very much.", "They're not.", "That's right, that's right." ]
['thank you', 'that is right', 'thank you very much', 'they are not', 'that is right that is right']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
but i but if there b are not like so it is it is start and end times just for the file
[ "But if they are, so it's not an end time just for the file.", "But if they are unlike so it's not an end time just for the file.", "But if they are unlike so it's not an N times just for the file.", "But if they are unlike, so it's not an end time just for the file.", "But if they are unlike so it's not an end times just for the file." ]
['but if they are so it is not an end time just for the file', 'but if they are unlike so it is not an end time just for the file', 'but if they are unlike so it is not an n times just for the file', 'but if they are unlike so it is not an end time just for the file', 'but if they are unlike so it is not an end times just for the file']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
just say we want some web space
[ "Just say we want some web space.", "Just say, we want some web space.", "Just say we want some webspace.", "Just say we want some Web Space.", "Just say, We want some web space." ]
['just say we want some web space', 'just say we want some web space', 'just say we want some webspace', 'just say we want some web space', 'just say we want some web space']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
how big are the chunks you are looking at
[ "How big are the chunks you're looking at?", "How big are the chunks you are looking at?", "How big are the trunks you're looking at?", "How big are the chunks you're looking at.", "How big are the trunks you are looking at?" ]
['how big are the chunks you are looking at', 'how big are the chunks you are looking at', 'how big are the trunks you are looking at', 'how big are the chunks you are looking at', 'how big are the trunks you are looking at']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
okay
[ "Okay.", "Ok.", "OK.", "Okay, okay.", "Okay," ]
['okay', 'ok', 'ok', 'okay okay', 'okay']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
all the different pieces but they say 0 this piece i leave out because it is below the current threshold level
[ "All the different pieces, but they say, oh, this piece I leave out because it's below their current threshold level.", "All the different pieces, but they say, oh, this piece I leave out because it's below the current threshold level.", "All the different pieces, but they say, oh this piece I leave out because it's below their current threshold level.", "All the different pieces, but they say, oh, this piece I leave out, because it's below their current threshold level.", "All the different pieces, but they say oh, this piece I leave out because it's below their current threshold level." ]
['all the different pieces but they say 0 this piece i leave out because it is below their current threshold level', 'all the different pieces but they say 0 this piece i leave out because it is below the current threshold level', 'all the different pieces but they say 0 this piece i leave out because it is below their current threshold level', 'all the different pieces but they say 0 this piece i leave out because it is below their current threshold level', 'all the different pieces but they say 0 this piece i leave out because it is below their current threshold level']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah we could do that
[ "Yeah, Well.", "Yeah, Well, Still.", "Yeah, Well, Keep going.", "Yeah, Well, You know.", "Yeah, Well, Can you tell?" ]
['yeah well', 'yeah well still', 'yeah well keep going', 'yeah well you know', 'yeah well can you tell']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
that that would be a named entity which is very highly fr frequented in that part
[ "That that would be a named entity which is very highly frequented in that part.", "That that would be a named entity, which is very highly frequented in that part.", "That would be a named entity which is very highly frequented in that part.", "That, that would be a named entity, which is very highly frequented in that part.", "That would be a named entity, which is very highly frequented in that part." ]
['that that would be a named entity which is very highly frequented in that part', 'that that would be a named entity which is very highly frequented in that part', 'that would be a named entity which is very highly frequented in that part', 'that that would be a named entity which is very highly frequented in that part', 'that would be a named entity which is very highly frequented in that part']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i do not see why it would be any more massive than the file
[ "I don't see why it'd be any more massive than the file.", "I don't see why I'd be any more massive than the file.", "I don't see why it would be any more massive than the file.", "I don't see why it be any more massive than the file.", "I don't see why it'd be any more massive than the file," ]
['i do not see why it would be any more massive than the file', 'i do not see why i would be any more massive than the file', 'i do not see why it would be any more massive than the file', 'i do not see why it be any more massive than the file', 'i do not see why it would be any more massive than the file']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
okay
[ "Okay.", "Ok.", "Okay,", "OK.", "okay." ]
['okay', 'ok', 'okay', 'ok', 'okay']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
anyway sorry
[ "Anyway, sorry.", "Anyway. Sorry.", "Anyway sorry.", "Anyway, I'm sorry.", "Yeah, Anyway. Sorry." ]
['anyway sorry', 'anyway sorry', 'anyway sorry', 'anyway i am sorry', 'yeah anyway sorry']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
topic s topic segments i meant
[ "topic segments I meant.", "topic segments I mentioned.", "topics, topics segments I meant.", "topic segments, I meant.", "topics, topic segments I meant." ]
['topic segments i meant', 'topic segments i mentioned', 'topics topics segments i meant', 'topic segments i meant', 'topics topic segments i meant']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so
[ "So, Uh.", "So um,", "So, Uh,", "So um.", "So uh," ]
['so', 'so', 'so', 'so', 'so']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
you know
[ "You know.", "You know?", "You know,", "you know.", "you know," ]
['you know', 'you know', 'you know', 'you know', 'you know']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah sure
[ "Yeah, sure.", "Yeah sure.", "Yes, sure.", "Yeah. Sure.", "yeah, sure." ]
['yeah sure', 'yeah sure', 'yes sure', 'yeah sure', 'yeah sure']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
that is the problem
[ "That's the problem.", "that's the problem.", "That is the problem.", "That's the problem, yeah.", "that's the problem," ]
['that is the problem', 'that is the problem', 'that is the problem', 'that is the problem yeah', 'that is the problem']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
you could just probably just add the final 2nd time to the next meeting and so on and just put it all together
[ "You could just probably just add the final second time to the next meeting and so on and just put it all together.", "You could just probably just add the final second time to the next meeting, and so on and just put it all together.", "You could just, probably just add the final second time to the next meeting and so on and just put it all together.", "You could just probably just add the final second time to the next meeting. And so on and just put it all together.", "You could just probably just add the final second time to the next meeting, and so on, and just put it all together." ]
['you could just probably just add the final 2nd time to the next meeting and so on and just put it all together', 'you could just probably just add the final 2nd time to the next meeting and so on and just put it all together', 'you could just probably just add the final 2nd time to the next meeting and so on and just put it all together', 'you could just probably just add the final 2nd time to the next meeting and so on and just put it all together', 'you could just probably just add the final 2nd time to the next meeting and so on and just put it all together']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
what i am like what we have to think about is if we go with this multi level idea
[ "What I'm like what we have to think about is if we go with this multi level idea.", "What I'm like, what we have to think about is if we go with this multi level idea.", "What I'm like what we have to think about is if we go with this multilevel idea.", "What I'm like, what we have to think about is if we go with this multilevel idea.", "What I'm like what we have to think about is if we go with this multi level ideas." ]
['what i am like what we have to think about is if we go with this multi level idea', 'what i am like what we have to think about is if we go with this multi level idea', 'what i am like what we have to think about is if we go with this multilevel idea', 'what i am like what we have to think about is if we go with this multilevel idea', 'what i am like what we have to think about is if we go with this multi level ideas']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
ma maybe there is some merit of going altogether for utterance level and not even bother to calculate i mean if you have to do it internally
[ "Maybe there's some merit of going all together for utterance level and not even bother to calculate. I mean, if you have to do it internally.", "Maybe there's some merit of going all together for utterance level, not even bother to calculate. I mean, if you have to do it internally.", "Maybe there's some merit of going altogether for utterance level and not even bother to calculate. I mean, if you have to do it internally.", "Maybe there's some merit of going altogether for utterance level, not even bother to calculate. I mean, if you have to do it internally.", "Maybe there's some merit of going all together for utterance level. I'm not even bothered to calculate. I mean, if you have to do it internally." ]
['maybe there is some merit of going all together for utterance level and not even bother to calculate i mean if you have to do it internally', 'maybe there is some merit of going all together for utterance level not even bother to calculate i mean if you have to do it internally', 'maybe there is some merit of going altogether for utterance level and not even bother to calculate i mean if you have to do it internally', 'maybe there is some merit of going altogether for utterance level not even bother to calculate i mean if you have to do it internally', 'maybe there is some merit of going all together for utterance level i am not even bothered to calculate i mean if you have to do it internally']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah i w i w i would need the raw text pretty soon because i have to find out how i have to put the segments into bins
[ "I would need the raw text pretty soon because I have to find out how I have to put the segments into bins.", "I would need the raw text pretty soon, because I have to find out how I have to put the segments into bins.", "Yeah, I would need the raw text pretty soon because I have to find out how I have to put the segments into bins.", "Yeah, I I would need the raw text pretty soon because I have to find out how I have to put the segments into bins.", "Yeah, I w I would need the raw text pretty soon because I have to find out how I have to put the segments into bins." ]
['i would need the raw text pretty soon because i have to find out how i have to put the segments into bins', 'i would need the raw text pretty soon because i have to find out how i have to put the segments into bins', 'yeah i would need the raw text pretty soon because i have to find out how i have to put the segments into bins', 'yeah i i would need the raw text pretty soon because i have to find out how i have to put the segments into bins', 'yeah i w i would need the raw text pretty soon because i have to find out how i have to put the segments into bins']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so if you could compress it and just put it into a temp directory
[ "So if you could compress it and just put it into attempt direction.", "So if you can compress it and just put it into attempt direction.", "So if you could compress it and just put it into a temp directory.", "So if you can compress it and just put it into a temp directory.", "So if you could compress it and just put it into a temp direction." ]
['so if you could compress it and just put it into attempt direction', 'so if you can compress it and just put it into attempt direction', 'so if you could compress it and just put it into a temp directory', 'so if you can compress it and just put it into a temp directory', 'so if you could compress it and just put it into a temp direction']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah you are able to do that in java yeah
[ "And you're able to do that in trouble, yeah.", "And your people still got in trouble, yeah.", "And you're able to do that in trouble, yeah?", "And you're able to do that in trouble, you?", "And you're able to do that in trouble, you." ]
['and you are able to do that in trouble yeah', 'and your people still got in trouble yeah', 'and you are able to do that in trouble yeah', 'and you are able to do that in trouble you', 'and you are able to do that in trouble you']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Yeah.", "Yes.", "Mm.", "Yeah,", "Yes," ]
['yeah', 'yes', '', 'yeah', 'yes']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so did did you have to combine them all and and then re order them
[ "Did you have to combine them all and then reorder them?", "Did did you have to combine them all and and then reorder them?", "Did you have to combine them all and and then reorder them?", "Did did you have to combine them all and then reorder them?", "So did you have to combine them all and then reorder them?" ]
['did you have to combine them all and then reorder them', 'did did you have to combine them all and and then reorder them', 'did you have to combine them all and and then reorder them', 'did did you have to combine them all and then reorder them', 'so did you have to combine them all and then reorder them']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
is that
[ "It's up.", "Is up.", "is up.", "it's up.", "It is up." ]
['it is up', 'is up', 'is up', 'it is up', 'it is up']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
ah yeah but that would have to be the temp directory off the machine you can s s h into directory of s s h
[ "But that would have to be the temp directory of the machine you can SSH into directly off SSH.", "But that would have to be the temp directory of the machine you can SSH into directly of SSH.", "Ah yeah, but that would have to be the temp directory of the machine you can SSH into directly off SSH.", "Ah, yeah, but that would have to be the temp directory of the machine you can SSH into directly off SSH.", "Ah yeah, but that would have to be the temp directory of the machine you can SSH into directly of SSH." ]
['but that would have to be the temp directory of the machine you can ssh into directly off ssh', 'but that would have to be the temp directory of the machine you can ssh into directly of ssh', 'ah yeah but that would have to be the temp directory of the machine you can ssh into directly off ssh', 'ah yeah but that would have to be the temp directory of the machine you can ssh into directly off ssh', 'ah yeah but that would have to be the temp directory of the machine you can ssh into directly of ssh']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
is that guaranteed to stay the
[ "Is that guaranteed to stay there?", "Is that guaranteed to stay there.", "Does that guarantee to stay there?", "is that guaranteed to stay there?", "Is that guarantee to stay there?" ]
['is that guaranteed to stay there', 'is that guaranteed to stay there', 'does that guarantee to stay there', 'is that guaranteed to stay there', 'is that guarantee to stay there']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah i am
[ "Yeah.", "Yeah,", "Yay.", "And.", "Yes." ]
['yeah', 'yeah', 'yay', 'and', 'yes']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i think it is cause we had to specify it ourselves that it is not as like focus the specification of most work we have to do
[ "I think it's because we had to specify it ourselves that it's not as, um, like focus or specification of most, um, work we have to do.", "I think it's because we had to specify it ourselves, that it's not as, um, like focus or specification of most, um, work we have to do.", "I think it's because we had to specify it ourselves that it's not as, um, like focused specification of most, um, work we have to do.", "I think it's because we had to specify it ourselves that it's not as, um, like, focus or specification of most, um, work we have to do.", "I think it's because we had to specify it ourselves, that it's not as, um, like focused specification of most, um, work we have to do." ]
['i think it is because we had to specify it ourselves that it is not as like focus or specification of most work we have to do', 'i think it is because we had to specify it ourselves that it is not as like focus or specification of most work we have to do', 'i think it is because we had to specify it ourselves that it is not as like focused specification of most work we have to do', 'i think it is because we had to specify it ourselves that it is not as like focus or specification of most work we have to do', 'i think it is because we had to specify it ourselves that it is not as like focused specification of most work we have to do']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
was it
[ "What's up?", "What's up.", "What's so?", "Was it?", "What's that?" ]
['what is up', 'what is up', 'what is so', 'was it', 'what is that']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i do not know
[ "I don't know.", "Don't know.", "You don't know.", "I don't know,", "I do not know." ]
['i do not know', 'do not know', 'you do not know', 'i do not know', 'i do not know']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
the basic word importance is off line as well
[ "The basic word importance is offline as well.", "The basic word important is offline as well.", "The basic word importance is offline, as well.", "The basic word importance is off line as well.", "the basic word importance is offline as well." ]
['the basic word importance is offline as well', 'the basic word important is offline as well', 'the basic word importance is offline as well', 'the basic word importance is off line as well', 'the basic word importance is offline as well']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
it could be a meeting
[ "it could be a meeting.", "It could be a meeting.", "there could be a meeting.", "it could be a meeting,", "There could be a meeting." ]
['it could be a meeting', 'it could be a meeting', 'there could be a meeting', 'it could be a meeting', 'there could be a meeting']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah but yeah but it it it failed right when you load it right the nite x m l kit so that is interesting
[ "Yeah, but yeah, but it fails right when you load it, right? The Nitex Melkit, so that's interesting.", "Yeah, but yeah, but it fails right when you load it, right? The Nitex Melkit. So that's interesting.", "Yeah, but yeah, but it failed right when you load it, right? The Nitex Melkit. So that's interesting.", "Yeah, but yeah, but it fails right when you load it, right? The Night Xmail kit. So that's interesting.", "Yeah, but yeah, but it failed right when you load it, right? The Night Xmail kit. So that's interesting." ]
['yeah but yeah but it fails right when you load it right the nitex melkit so that is interesting', 'yeah but yeah but it fails right when you load it right the nitex melkit so that is interesting', 'yeah but yeah but it failed right when you load it right the nitex melkit so that is interesting', 'yeah but yeah but it fails right when you load it right the night xmail kit so that is interesting', 'yeah but yeah but it failed right when you load it right the night xmail kit so that is interesting']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
they have to read numbers from
[ "Mm, They have to read numbers from.", "Mmm they have to read numbers from.", "Mmm, They have to read numbers from.", "Mm. They have to read numbers from.", "Mmm, they have to read numbers from." ]
['they have to read numbers from', 'they have to read numbers from', 'they have to read numbers from', 'they have to read numbers from', 'they have to read numbers from']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so
[ "Sorry.", "Sir.", "sorry.", "So.", "Sorry, Sir." ]
['sorry', 'sir', 'sorry', 'so', 'sorry sir']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i will p i will probably ask jonathan about it
[ "I'll probably ask Jonathan about it.", "I'll I'll probably ask Jonathan about it.", "Um, I'll I'll probably ask Jonathan about it.", "Um, I'll, I'll probably ask Jonathan about it.", "Um I'll I'll probably ask Jonathan about it." ]
['i will probably ask jonathan about it', 'i will i will probably ask jonathan about it', 'i will i will probably ask jonathan about it', 'i will i will probably ask jonathan about it', 'i will i will probably ask jonathan about it']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
but
[ "three.", "That's right.", "Then,", "That's right,", "That's right. That's right." ]
['3', 'that is right', 'then', 'that is right', 'that is right that is right']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah or but also like all these elements like like the loading and yeah integration and and like handling the data loading and stuff
[ "Yeah, but also like all these elements like like the loading and yeah integration and like handling the data loading and stuff.", "Yeah, but also like all these elements, like like the loading and yeah integration and like handling the data loading and stuff.", "Yeah. But also like all these elements like like the loading and yeah integration and like handling the data loading and stuff.", "Yeah, but also like all these elements like the loading and integration and like handling the data loading and stuff.", "Yeah, but also like all these elements, like the loading and integration and like handling the data loading and stuff." ]
['yeah but also like all these elements like like the loading and yeah integration and like handling the data loading and stuff', 'yeah but also like all these elements like like the loading and yeah integration and like handling the data loading and stuff', 'yeah but also like all these elements like like the loading and yeah integration and like handling the data loading and stuff', 'yeah but also like all these elements like the loading and integration and like handling the data loading and stuff', 'yeah but also like all these elements like the loading and integration and like handling the data loading and stuff']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
you know what i mean like not not the whole corpus but every meeting that has to do with one topic
[ "Do you know what I mean? Like not not the whole corpus, but every meeting that has to do with one topic.", "Do you know what I mean? Like not not the whole corpus but every meeting that has to do with one topic.", "You know what I mean? Like not not the whole corpus, but every meeting that has to do with one topic.", "Do you know what I mean? Like, not not the whole corpus, but every meeting that has to do with one topic.", "You know what I mean? Like, not not the whole corpus, but every meeting that has to do with one topic." ]
['do you know what i mean like not not the whole corpus but every meeting that has to do with one topic', 'do you know what i mean like not not the whole corpus but every meeting that has to do with one topic', 'you know what i mean like not not the whole corpus but every meeting that has to do with one topic', 'do you know what i mean like not not the whole corpus but every meeting that has to do with one topic', 'you know what i mean like not not the whole corpus but every meeting that has to do with one topic']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
like if if there is a lot of bureaucracy involved with having 2 different trees and whether one ties to the other because the one has the weight for the other
[ "Like if there's a lot of bureaucracy involved with having two different trees and where the one ties to the other because the one has to wait for the other.", "Like, if there's a lot of bureaucracy involved with having two different trees and where the one ties to the other, because the one has to wait for the other.", "Like if there's a lot of bureaucracy involved with having two different trees and where the one ties to the other, because the one has to wait for the other.", "Like, if there's a lot of bureaucracy involved with having two different trees and where the one ties to the other because the one has to wait for the other.", "Like, if there's a lot of bureaucracy involved with having two different trees, and where the one ties to the other, because the one has to wait for the other." ]
['like if there is a lot of bureaucracy involved with having 2 different trees and where the one ties to the other because the one has to wait for the other', 'like if there is a lot of bureaucracy involved with having 2 different trees and where the one ties to the other because the one has to wait for the other', 'like if there is a lot of bureaucracy involved with having 2 different trees and where the one ties to the other because the one has to wait for the other', 'like if there is a lot of bureaucracy involved with having 2 different trees and where the one ties to the other because the one has to wait for the other', 'like if there is a lot of bureaucracy involved with having 2 different trees and where the one ties to the other because the one has to wait for the other']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
there is a there is a serialize command so that gives me one mega mother of a s
[ "There's a serialized command, so that gives me one mega mother of a.", "There's a serialized command so that gives me one mega mother of a.", "There's a serialized command. So that gives me one mega mother of a.", "There's a serialized command, so that gives me one mega mother of a", "There is a serialized command, so that gives me one mega mother of a." ]
['there is a serialized command so that gives me one mega mother of a', 'there is a serialized command so that gives me one mega mother of a', 'there is a serialized command so that gives me one mega mother of a', 'there is a serialized command so that gives me one mega mother of a', 'there is a serialized command so that gives me one mega mother of a']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Yeah.", "Yes.", "yeah.", "Yeah, yeah.", "Yep." ]
['yeah', 'yes', 'yeah', 'yeah yeah', 'yep']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
no
[ "No.", "no.", "Nope.", "Not.", "No, no." ]
['no', 'no', 'nope', 'not', 'no no']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Yeah.", "Yes.", "Yeah,", "yeah.", "Yea." ]
['yeah', 'yes', 'yeah', 'yeah', 'yea']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
add add the structure yeah
[ "At the structure, yeah.", "At the structure here.", "At the structure. Yeah.", "At the structure yeah.", "At at the structure, yeah." ]
['at the structure yeah', 'at the structure here', 'at the structure yeah', 'at the structure yeah', 'at at the structure yeah']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
okay so yeah so we have those and and then we have some f field somewhere else which has
[ "Okay, so yeah, so we have those and and then we have some field somewhere else which has", "Okay, so yeah, so we have those and then we have some field somewhere else which has", "Okay, so yeah, so we have those and and then we have some field somewhere else which has.", "Okay, so yeah, so we have those and then we have some field somewhere else which has.", "Okay, so yeah, so we have those, and and then we have some field somewhere else which has" ]
['okay so yeah so we have those and and then we have some field somewhere else which has', 'okay so yeah so we have those and then we have some field somewhere else which has', 'okay so yeah so we have those and and then we have some field somewhere else which has', 'okay so yeah so we have those and then we have some field somewhere else which has', 'okay so yeah so we have those and and then we have some field somewhere else which has']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
process any arbitrary x m l
[ "process any arbitrary XML.", "process any arbitrary XMLs.", "Process any arbitrary XML.", "process any arbitrary XML's.", "Process any arbitrary XMLs." ]
['process any arbitrary xml', 'process any arbitrary xmls', 'process any arbitrary xml', 'process any arbitrary xml is', 'process any arbitrary xmls']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
right because it is sort of like it is just a time window
[ "Right, because it's sort of like it's just a time window.", "Right? Because it's sort of like it's just a time window.", "Right. Because it's sort of like it's just a time window.", "Right, because it's sort of like, it's just a time window.", "Right? Because it's sort of like, it's just a time window." ]
['right because it is sort of like it is just a time window', 'right because it is sort of like it is just a time window', 'right because it is sort of like it is just a time window', 'right because it is sort of like it is just a time window', 'right because it is sort of like it is just a time window']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
0 yeah yeah
[ "Oh yeah, yeah.", "Oh, yeah, yeah.", "Oh yeah. Yeah.", "Oh yeah yeah.", "Oh, yeah. Yeah." ]
['0 yeah yeah', '0 yeah yeah', '0 yeah yeah', '0 yeah yeah', '0 yeah yeah']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
it is just working on an arbitrary text file at the moment
[ "It's just working on an arbitrary text file at the moment.", "But it's just working on an arbitrary text file at the moment.", "it's just working on an arbitrary text file at the moment.", "So it's just working on an arbitrary text file at the moment.", "Just working on an arbitrary text file at the moment." ]
['it is just working on an arbitrary text file at the moment', 'but it is just working on an arbitrary text file at the moment', 'it is just working on an arbitrary text file at the moment', 'so it is just working on an arbitrary text file at the moment', 'just working on an arbitrary text file at the moment']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
and there is segments which
[ "And there is segments which.", "And there is segments, which.", "And there is segments which hmm.", "And there is segments which...", "And there is segments which. Hmm." ]
['and there is segments which', 'and there is segments which', 'and there is segments which', 'and there is segments which .', 'and there is segments which']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i know
[ "kind of", "kind of,", "kind of.", "Kind of.", "kind of a" ]
['kind of', 'kind of', 'kind of', 'kind of', 'kind of a']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Yeah.", "Yeah,", "yeah.", "Yes.", "Yah." ]
['yeah', 'yeah', 'yeah', 'yes', 'yah']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
that probably does not make any sense at all whereas if you just show important utterances but the utterance as a whole it makes more sense
[ "It probably doesn't make any sense at all, whereas if you just show important utterances, but the utterance as a whole, it makes more sense.", "It probably doesn't make any sense at all whereas if you just show important utterances, but the utterance as a whole, it makes more sense.", "That probably doesn't make any sense at all, whereas if you just show important utterances, but the utterance as a whole, it makes more sense.", "It probably doesn't make any sense at all, whereas if you just show important utterances but the utterance as a whole, it makes more sense.", "It probably doesn't make any sense at all, whereas if you just show important utterances, but the utterance as a whole makes more sense." ]
['it probably does not make any sense at all whereas if you just show important utterances but the utterance as a whole it makes more sense', 'it probably does not make any sense at all whereas if you just show important utterances but the utterance as a whole it makes more sense', 'that probably does not make any sense at all whereas if you just show important utterances but the utterance as a whole it makes more sense', 'it probably does not make any sense at all whereas if you just show important utterances but the utterance as a whole it makes more sense', 'it probably does not make any sense at all whereas if you just show important utterances but the utterance as a whole makes more sense']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah actually i need the raw text as well
[ "Yeah, clear, I need the rotics as well.", "Yeah, clear. I need the rotics as well.", "Yeah, clearly I need the rotics as well.", "Yeah, clear I need the rotics as well.", "Yeah, clear, I need the rockets as well." ]
['yeah clear i need the rotics as well', 'yeah clear i need the rotics as well', 'yeah clearly i need the rotics as well', 'yeah clear i need the rotics as well', 'yeah clear i need the rockets as well']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
because actually i mean probably users would not view that one too often
[ "Because actually, I mean, probably users wouldn't view that one too often.", "Because actually, probably users wouldn't view that one too often.", "Because actually, I mean probably users wouldn't view that one too often.", "Because actually I mean probably users wouldn't view that one too often.", "Because, actually, I mean, probably users wouldn't view that one too often." ]
['because actually i mean probably users would not view that one too often', 'because actually probably users would not view that one too often', 'because actually i mean probably users would not view that one too often', 'because actually i mean probably users would not view that one too often', 'because actually i mean probably users would not view that one too often']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so no i did not i did not mean tree
[ "I didn't mean trees.", "I didn't mean tree.", "I didn't I didn't mean trees.", "Senator, I didn't mean trees.", "I didn't I didn't mean tree." ]
['i did not mean trees', 'i did not mean tree', 'i did not i did not mean trees', 'senator i did not mean trees', 'i did not i did not mean tree']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i wa i was just worried about the total memory complexity of it
[ "I was just worried about the total memory complexity of it.", "I I was just worried about the total memory complexity of it.", "I just worried about the total memory complexity of it.", "I, I was just worried about the total memory complexity of it.", "I was just worried about the total memory complexity of it. It's a good idea." ]
['i was just worried about the total memory complexity of it', 'i i was just worried about the total memory complexity of it', 'i just worried about the total memory complexity of it', 'i i was just worried about the total memory complexity of it', 'i was just worried about the total memory complexity of it it is a good idea']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
is the is that the same as utterances that is that the same as utterances that
[ "Is that the same as utterance, is that the same as utterance, is that?", "Is the is that the same as utterance is that is that the same as utterance is that.", "Is the is that the same as utterance is that is that the same as utterance is that?", "Is is that the same as utterance, is that is that the same as utterance, is that?", "Is that is that the same as utterance, is that is that the same as utterance, is that?" ]
['is that the same as utterance is that the same as utterance is that', 'is the is that the same as utterance is that is that the same as utterance is that', 'is the is that the same as utterance is that is that the same as utterance is that', 'is is that the same as utterance is that is that the same as utterance is that', 'is that is that the same as utterance is that is that the same as utterance is that']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
is that a board marker pen actually
[ "Instead a board marker pen actually.", "Instead a board marker pen, actually.", "Instead of board marker pen, actually.", "Instead of a board marker pen, actually.", "Instead of board marker pen actually." ]
['instead a board marker pen actually', 'instead a board marker pen actually', 'instead of board marker pen actually', 'instead of a board marker pen actually', 'instead of board marker pen actually']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so generally dictionaries can grow bigger then you think they do
[ "So generally dictionaries can grow bigger than you think they do.", "So, generally dictionaries can grow bigger than you think they do.", "So generally, dictionaries can grow bigger than you think they do.", "So, generally, dictionaries can grow bigger than you think they do.", "So, Generally dictionaries can grow bigger than you think they do." ]
['so generally dictionaries can grow bigger than you think they do', 'so generally dictionaries can grow bigger than you think they do', 'so generally dictionaries can grow bigger than you think they do', 'so generally dictionaries can grow bigger than you think they do', 'so generally dictionaries can grow bigger than you think they do']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
right so you it is same you would start with the leaves and you go 0 i want a topic segment
[ "Right. So you same. You'd start with the leads and you'd go, I want the topic segment.", "Right. So you same, you'd start with the leads and you'd go, I want the topic segment.", "Right. So you same. You'd start with the leaves and you'd go, I want the topic segment.", "Right. So you, it's the same. You'd start with the leads and you'd go, I want the topic segment.", "Right. So you, it's the same. You'd start with the leads and you'd go, I want a topic segment." ]
['right so you same you would start with the leads and you would go i want the topic segment', 'right so you same you would start with the leads and you would go i want the topic segment', 'right so you same you would start with the leaves and you would go i want the topic segment', 'right so you it is the same you would start with the leads and you would go i want the topic segment', 'right so you it is the same you would start with the leads and you would go i want a topic segment']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
well no it does not have to be
[ "Well, not it doesn't have to be.", "Well, No, It doesn't have to be.", "Well not it doesn't have to be.", "Well not, it doesn't have to be.", "Well, Not it doesn't have to be." ]
['well not it does not have to be', 'well no it does not have to be', 'well not it does not have to be', 'well not it does not have to be', 'well not it does not have to be']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i think so
[ "I think so.", "Yeah, I think so.", "Yeah I think so.", "Yeah. I think so.", "I think so," ]
['i think so', 'yeah i think so', 'yeah i think so', 'yeah i think so', 'i think so']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
like just build an another f wave file essentially
[ "Like just build an another wave file essentially.", "Like just build another wave file essentially.", "Like just build an another wave fire essentially.", "Like, just build an another wave file essentially.", "Like just build an another wave fight essentially." ]
['like just build an another wave file essentially', 'like just build another wave file essentially', 'like just build an another wave fire essentially', 'like just build an another wave file essentially', 'like just build an another wave fight essentially']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
okay
[ "Okay.", "Ok.", "OK.", "Okay,", "Okay?" ]
['okay', 'ok', 'ok', 'okay', 'okay']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
but one of these big corpuses has a list of stop words that you can download and they are just basically lists of really uninteresting boring words that we could filter out before we do that
[ "But one of these big corpuses has a list of stop words that you can download and they're just basically lists of really uninteresting, boring words that we could filter out before we do that.", "But one of these big corpuses has a list of stop words that you can download and they're just basically lists of really uninteresting boring words that we could filter out before we do that.", "But one of these big corpuses has a list of stop words that you can download, and they're just basically lists of really uninteresting, boring words that we could filter out before we do that.", "But one of these big corpuses has a list of stopwords that you can download and they're just basically lists of really uninteresting, boring words that we could filter out before we do that.", "But one of these big corpuses has a list of stop words that you can download and they're just basically list of really uninteresting, boring words that we could filter out before we do that." ]
['but one of these big corpuses has a list of stop words that you can download and they are just basically lists of really uninteresting boring words that we could filter out before we do that', 'but one of these big corpuses has a list of stop words that you can download and they are just basically lists of really uninteresting boring words that we could filter out before we do that', 'but one of these big corpuses has a list of stop words that you can download and they are just basically lists of really uninteresting boring words that we could filter out before we do that', 'but one of these big corpuses has a list of stopwords that you can download and they are just basically lists of really uninteresting boring words that we could filter out before we do that', 'but one of these big corpuses has a list of stop words that you can download and they are just basically list of really uninteresting boring words that we could filter out before we do that']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
kay
[ "Okay.", "OK.", "Today.", "Okay,", "Together." ]
['okay', 'ok', 'today', 'okay', 'together']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so yeah we may as well collaborate
[ "So, Uh, We may as well collaborate.", "So, Yeah, We may as well collaborate.", "So we may as well collaborate.", "So, Eh, We may as well collaborate.", "So yeah, we may as well collaborate." ]
['so we may as well collaborate', 'so yeah we may as well collaborate', 'so we may as well collaborate', 'so eh we may as well collaborate', 'so yeah we may as well collaborate']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
you just like create a new x m l document in memory
[ "You just like create a new XML document in memory.", "Just like create a new XML document in memory.", "you just like create a new XML document in memory.", "You just like, create a new XML document in memory.", "just like create a new XML document in memory." ]
['you just like create a new xml document in memory', 'just like create a new xml document in memory', 'you just like create a new xml document in memory', 'you just like create a new xml document in memory', 'just like create a new xml document in memory']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Yeah.", "Mm.", "No.", "Yes.", "Yeah," ]
['yeah', '', 'no', 'yes', 'yeah']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah but i am i am still worried
[ "Yeah, but I'm still worried.", "Yeah, but I am still worried.", "Yeah but I'm still worried.", "Yeah. But I'm still worried.", "Yes, but I'm still worried." ]
['yeah but i am still worried', 'yeah but i am still worried', 'yeah but i am still worried', 'yeah but i am still worried', 'yes but i am still worried']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah but the thing is
[ "Yeah, but the thing is.", "Yeah, but the thing is, um.", "Yeah, the thing is.", "Yeah, but the thing is, um,", "Yeah, but the thing is um." ]
['yeah but the thing is', 'yeah but the thing is', 'yeah the thing is', 'yeah but the thing is', 'yeah but the thing is']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
i mean what you do is you just build an x m l file and if you want it to get down to the utterances you would go to the leaves
[ "I mean, what you do is you just build an XML file, and if you wanted to get down to the utterances, you'd go to the leaves.", "I mean, what you do is you just build an XML file and if you wanted to get down to the utterances, you'd go to the leaves.", "I mean, what you'd do is you'd just build an XML file and if you wanted to get down to the utterances, you'd go to the leaves.", "I mean, what you do is you just build an XML file. And if you wanted to get down to the utterances, you'd go to the leaves.", "I mean, what you do is you just build an XML file and if you wanted to get down to the utterances you'd go to the leaves." ]
['i mean what you do is you just build an xml file and if you wanted to get down to the utterances you would go to the leaves', 'i mean what you do is you just build an xml file and if you wanted to get down to the utterances you would go to the leaves', 'i mean what you would do is you would just build an xml file and if you wanted to get down to the utterances you would go to the leaves', 'i mean what you do is you just build an xml file and if you wanted to get down to the utterances you would go to the leaves', 'i mean what you do is you just build an xml file and if you wanted to get down to the utterances you would go to the leaves']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Yeah.", "Yes.", "Yeah,", "Yeah?", "Yep." ]
['yeah', 'yes', 'yeah', 'yeah', 'yep']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
okay
[ "Okay.", "Ok.", "OK.", "Okay,", "okay." ]
['okay', 'ok', 'ok', 'okay', 'okay']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
it is the same thing if like whether you play and it moves forward or whether you jump to a position through search
[ "It's the same thing if like whether you play and it moves forward or whether you jump to a position through search.", "It's the same thing if like whether you play and it moves forward, or whether you jump to a position through search.", "It's the same thing if, like whether you play and it moves forward or whether you jump to a position through search.", "It's the same thing if, like, whether you play and it moves forward or whether you jump to a position through search.", "It's the same thing if like, whether you play and it moves forward or whether you jump to a position through search." ]
['it is the same thing if like whether you play and it moves forward or whether you jump to a position through search', 'it is the same thing if like whether you play and it moves forward or whether you jump to a position through search', 'it is the same thing if like whether you play and it moves forward or whether you jump to a position through search', 'it is the same thing if like whether you play and it moves forward or whether you jump to a position through search', 'it is the same thing if like whether you play and it moves forward or whether you jump to a position through search']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
because i am i think i will also i could also make use of it for the agreement and disagreement thing
[ "Because I'm I think I will also I could also make use of it for the agreement and disagreement thing.", "Because I think I will also I could also make use of it for the agreement and disagreement thing.", "Because I'm, I think I will also, I could also make use of it for the agreement and disagreement thing.", "Because I'm I think I will also, I could also make use of it for the agreement and disagreement thing.", "Because I'm I think I will also have I could also make use of it for the agreement and disagreement thing." ]
['because i am i think i will also i could also make use of it for the agreement and disagreement thing', 'because i think i will also i could also make use of it for the agreement and disagreement thing', 'because i am i think i will also i could also make use of it for the agreement and disagreement thing', 'because i am i think i will also i could also make use of it for the agreement and disagreement thing', 'because i am i think i will also have i could also make use of it for the agreement and disagreement thing']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Yeah.", "Year.", "year.", "Yes.", "Yeah," ]
['yeah', 'year', 'year', 'yes', 'yeah']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah prob
[ "your property.", "Your problem.", "your problem.", "Your property.", "Your proper" ]
['your property', 'your problem', 'your problem', 'your property', 'your proper']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
so we just we are sort of running different types of queries on it
[ "So we just we were sort of running different types of queries on it.", "So we just, we were sort of running different types of queries on it.", "So we were sort of running different types of queries on it.", "So we just were sort of running different types of queries on it.", "So we just, we're sort of running different types of queries on it." ]
['so we just we were sort of running different types of queries on it', 'so we just we were sort of running different types of queries on it', 'so we were sort of running different types of queries on it', 'so we just were sort of running different types of queries on it', 'so we just we are sort of running different types of queries on it']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
or un or try to understand how nite x m l i d s work and maybe there is some special route we have to follow when we use these i d s
[ "Or try to understand how Nitex and LIDs work and maybe there's some special rules we have to follow when we use these IDs.", "Or try to understand how Nitex and LIDs work, and maybe there's some special rules we have to follow when we use these IDs.", "Or try to understand how Nitex and LIDs work and maybe there's some special rules we have to follow when we use these ID's.", "Or try to understand how Nitex and LIDs work, and maybe there's some special rules we have to follow when we use these ID's.", "Or try to understand how NIDEXML ID's work and maybe there's some special rules we have to follow when we use these ID's." ]
['or try to understand how nitex and lids work and maybe there is some special rules we have to follow when we use these ids', 'or try to understand how nitex and lids work and maybe there is some special rules we have to follow when we use these ids', 'or try to understand how nitex and lids work and maybe there is some special rules we have to follow when we use these id is', 'or try to understand how nitex and lids work and maybe there is some special rules we have to follow when we use these id is', 'or try to understand how nidexml id is work and maybe there is some special rules we have to follow when we use these id is']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
what what does anyone want
[ "What does anyone want?", "what does anyone want?", "What does anyone want", "What does anyone want.", "what does anyone want." ]
['what does anyone want', 'what does anyone want', 'what does anyone want', 'what does anyone want', 'what does anyone want']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
alright
[ "Right.", "Right?", "right?", "right.", "Right, right." ]
['right', 'right', 'right', 'right', 'right right']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
that is 99 characters over okay
[ "Let's nine nine characters over okay.", "Let's nine nine characters over. Okay.", "That's nine, nine characters over. Okay.", "That's nine, nine characters over, okay.", "Let's nine, nine characters over, okay." ]
['let us 99 characters over okay', 'let us 99 characters over okay', 'that is 99 characters over okay', 'that is 99 characters over okay', 'let us 99 characters over okay']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
we go and look at nite x m l or some of us look at nite x m l in a bit more detail
[ "We go and look at Night Ex Mail or some of us look at Night Ex Mail in a bit more detail.", "We go and look at Night Xmail or some of us look at Night Xmail in a bit more detail.", "We go and look at Night Exmail or some of us look at Night Exmail in a bit more detail.", "We go and look at Night Ex Mail, or some of us look at Night Ex Mail in a bit more detail.", "We go and look at Night Exmail, or some of us look at Night Exmail in a bit more detail." ]
['we go and look at night ex mail or some of us look at night ex mail in a bit more detail', 'we go and look at night xmail or some of us look at night xmail in a bit more detail', 'we go and look at night exmail or some of us look at night exmail in a bit more detail', 'we go and look at night ex mail or some of us look at night ex mail in a bit more detail', 'we go and look at night exmail or some of us look at night exmail in a bit more detail']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
but i was thinking of the topic segmentation now and and f for that there would only be one
[ "But I was thinking of the topic segmentation now and for that there would only be one.", "But I was thinking of the topic segmentation now, and for that there would only be one.", "But I was thinking of the topic segmentation now and for that, there would only be one.", "But I was thinking of the topic segmentation now, and for that, there would only be one.", "I was thinking of the topic segmentation now and for that there would only be one." ]
['but i was thinking of the topic segmentation now and for that there would only be one', 'but i was thinking of the topic segmentation now and for that there would only be one', 'but i was thinking of the topic segmentation now and for that there would only be one', 'but i was thinking of the topic segmentation now and for that there would only be one', 'i was thinking of the topic segmentation now and for that there would only be one']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
because then we can also do the display about who is speaking
[ "Because then we can also do the display about who's speaking.", "because then we can also do the display about who's speaking.", "Because then we can also do the display about who is speaking.", "because then we can also do the display about who is speaking.", "Because then we can also do the display about who's speaking," ]
['because then we can also do the display about who is speaking', 'because then we can also do the display about who is speaking', 'because then we can also do the display about who is speaking', 'because then we can also do the display about who is speaking', 'because then we can also do the display about who is speaking']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
but i have only just got the notes
[ "But um, i've only just got the note.", "But um, i've only just got the note", "But um I've only just got the note.", "But um, I've only just got the note.", "But, Um, I've only just got the note." ]
['but i have only just got the note', 'but i have only just got the note', 'but i have only just got the note', 'but i have only just got the note', 'but i have only just got the note']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
yeah
[ "Yeah.", "yeah.", "Yes.", "Yea.", "Yeah," ]
['yeah', 'yeah', 'yes', 'yea', 'yeah']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
really
[ "Really?", "Really.", "really?", "really.", "really" ]
['really', 'really', 'really', 'really', 'really']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.
0 you do not want to have different counts for each chunk but just like sort of for for something from old chunks
[ "You don't want to have different counts for each chunk, but just like sort of four for something from all chunks.", "You don't want to have different counts for each chunk but just like sort of four for something from all chunks.", "You don't want to have different counts for each chunk, but just like sort of for something from all chunks.", "You don't want to have different counts for each chunk, but just like sort of four, for something from all chunks.", "You don't want to have different counts for each chunk but just like sort of for something from all chunks." ]
['you do not want to have different counts for each chunk but just like sort of 4 for something from all chunks', 'you do not want to have different counts for each chunk but just like sort of 4 for something from all chunks', 'you do not want to have different counts for each chunk but just like sort of for something from all chunks', 'you do not want to have different counts for each chunk but just like sort of 4 for something from all chunks', 'you do not want to have different counts for each chunk but just like sort of for something from all chunks']
The following text contains 5-best hypotheses from an Automatic Speech Recognition system. As part of a speech recognition task, please perform error correction on the hypotheses to generate the most accurate transcription of the spoken text.