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PeacefulData
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Neko-v1

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so that will that that that must be in it you think
[ "So that will that must be in it, you think.", "So that will, that must be in it, you think.", "So that will that must be in it, you think?", "So that will that that must be in it, you think.", "So that will that must be in it you think." ]
['so that will that must be in it you think', 'so that will that must be in it you think', 'so that will that must be in it you think', 'so that will that that must be in it you think', 'so that will that must be in it you think']
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 too
[ "Uh, you too?", "Uh, You too?", "Uh, You too.", "Ah, you too?", "Uh, You Too?" ]
['you too', 'you too', 'you too', 'ah you too', 'you too']
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.", "yeah.", "Yeah," ]
['yeah', 'yeah', 'yes', '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.
well
[ "Uh.", "Ah.", "Oh.", "Uh, yeah.", "Uh," ]
['', 'ah', '0', '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.
you read many times in magazines same kind of stuff and you heard on the news that that he yeah
[ "You read many times in magazines and kind of stuff, and you heard on the news that yeah.", "You read many times in magazines and kind of stuff, and you heard on the news that, yeah.", "You read many times in magazines and kind of stuff, and you heard on the news that that yeah.", "You read many times in magazines and kind of stuff, and you heard on the news that, uh, that, yeah.", "You read many times in magazines and kind of stuff, and you read on the news that, uh, that, yeah." ]
['you read many times in magazines and kind of stuff and you heard on the news that yeah', 'you read many times in magazines and kind of stuff and you heard on the news that yeah', 'you read many times in magazines and kind of stuff and you heard on the news that that yeah', 'you read many times in magazines and kind of stuff and you heard on the news that that yeah', 'you read many times in magazines and kind of stuff and you read on the news that that 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.
hey
[ "Hey.", "Hey, yeah.", "Aye.", "Ay.", "hey." ]
['hey', 'hey yeah', 'aye', 'ay', 'hey']
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.
who who gave you the master class
[ "Who who gave you the master class?", "Who Who gave you the master class?", "Who who gave you the master class.", "who who gave you the master class?", "Who who gave you the masterclass?" ]
['who who gave you the master class', 'who who gave you the master class', 'who who gave you the master class', 'who who gave you the master class', 'who who gave you the masterclass']
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 you can go for both
[ "Or you can go for both.", "Or you could go for both.", "or you can go for both.", "or you could go for both.", "Or, you could go for both." ]
['or you can go for both', 'or you could go for both', 'or you can go for both', 'or you could go for both', 'or you could go for both']
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.
fresh
[ "Fresh.", "fresh.", "Fresh?", "It's fresh.", "fresh?" ]
['fresh', 'fresh', 'fresh', 'it is fresh', 'fresh']
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 you have that in the
[ "Yeah, but you have that in the.", "Yeah, but you have that in the", "Yeah but you have that in the.", "Yeah, but you have that in the,", "Yeah. But you have that in the." ]
['yeah but you have that in the', 'yeah but you have that in the', 'yeah but you have that in the', 'yeah but you have that in the', 'yeah but you have that in the']
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 maybe i have something in my presentation to to cope with that
[ "Well, maybe I have something in my presentation to, uh, to cope with that.", "Well, maybe I have something in my presentation to cope with that.", "Well, maybe I have something in my presentation to uh, to cope with that.", "Well maybe I have something in my presentation to uh to cope with that.", "Well maybe I have something in my presentation to, uh, to cope with that." ]
['well maybe i have something in my presentation to to cope with that', 'well maybe i have something in my presentation to cope with that', 'well maybe i have something in my presentation to to cope with that', 'well maybe i have something in my presentation to to cope with that', 'well maybe i have something in my presentation to to cope with 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.
the 1st one is really about the the the the the total package with well
[ "The first one is really about the total package with well.", "The first one is really about the total package with a well.", "The first one is really about the the total package with well.", "First one is really about the total package with well.", "The first one is really about the total package with well," ]
['the 1st one is really about the total package with well', 'the 1st one is really about the total package with a well', 'the 1st one is really about the the total package with well', '1st one is really about the total package with well', 'the 1st one is really about the total package with 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.
so that is a kinda new thing
[ "So that's a kind of newer thing.", "So that's kind of newer thing.", "So that's kind of a newer thing.", "So, that's a kind of newer thing.", "So, that's kind of newer thing." ]
['so that is a kind of newer thing', 'so that is kind of newer thing', 'so that is kind of a newer thing', 'so that is a kind of newer thing', 'so that is kind of newer 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.
that is it
[ "That's it.", "That's it?", "that's it.", "That is it.", "That's it," ]
['that is it', 'that is it', 'that is it', 'that is it', 'that is 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.
also this time there will be 3 presentations
[ "Also this time there will be three presentations.", "Also, this time there will be three presentations.", "Also this time, there will be three presentations.", "Also, this time, there will be three presentations.", "And also this time there will be three presentations." ]
['also this time there will be 3 presentations', 'also this time there will be 3 presentations', 'also this time there will be 3 presentations', 'also this time there will be 3 presentations', 'and also this time there will be 3 presentations']
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 the nokia the removable covers just put a red on it and go to the shop and buy a green one
[ "Like the Nokia removal coffers, just put a red on it and go to the shop and buy a green one.", "Like the Nokia removal coffers, just put a red on it and go to shop and buy a green one.", "Like the Nokia removal coffers, just put a red on it, and go to the shop and buy a green one.", "Like the Nokia removal coffers. Just put a red on it and go to the shop and buy a green one.", "Like the Nokia removal coffins, just put a red on it and go to the shop and buy a green one." ]
['like the nokia removal coffers just put a red on it and go to the shop and buy a green one', 'like the nokia removal coffers just put a red on it and go to shop and buy a green one', 'like the nokia removal coffers just put a red on it and go to the shop and buy a green one', 'like the nokia removal coffers just put a red on it and go to the shop and buy a green one', 'like the nokia removal coffins just put a red on it and go to the shop and buy a green 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.
yeah something like that
[ "Something like that.", "Yeah, something like that.", "Yeah something like that.", "Yes, something like that.", "Yeah. Something like that." ]
['something like that', 'yeah something like that', 'yeah something like that', 'yes something like that', 'yeah something like 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 can not imagine how how how it looks like
[ "I can't imagine how it looks like.", "I can't imagine how how it looks like.", "I can't imagine how our looks like.", "I can't imagine how I would look like.", "I can't imagine how, how it looks like." ]
['i can not imagine how it looks like', 'i can not imagine how how it looks like', 'i can not imagine how our looks like', 'i can not imagine how i would look like', 'i can not imagine how how it looks like']
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 want me to draw in 3 d
[ "You want me to draw a free D.", "You want me to draw a three D.", "You want me to draw a Freedy.", "You want me to draw a free dee.", "You want me to draw a free die." ]
['you want me to draw a free d', 'you want me to draw a 3 d', 'you want me to draw a freedy', 'you want me to draw a free dee', 'you want me to draw a free die']
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.
1250
[ "Twelve fifty, yeah.", "Twelve fifty yeah.", "twelve fifty, yeah.", "Twelve fifty, yeah. Yeah.", "Twelve fifty yeah. Yeah." ]
['1250 yeah', '1250 yeah', '1250 yeah', '1250 yeah yeah', '1250 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.
t have some other options that are not programmable with one horizontal button
[ "Have some other options that are not programmable with one horizontal button.", "Um, have some other options that are not programmable with one horizontal button.", "I have some other options that are not programmable with one horizontal button.", "Um have some other options that are not programmable with one horizontal button.", "have some other options that are not programmable with one horizontal button." ]
['have some other options that are not programmable with one horizontal button', 'have some other options that are not programmable with one horizontal button', 'i have some other options that are not programmable with one horizontal button', 'have some other options that are not programmable with one horizontal button', 'have some other options that are not programmable with one horizontal button']
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 material the younger people want aint it
[ "That's the material the younger people want.", "That's the material the younger people want to ain't it?", "That's the material the younger people want. Ain't it?", "That's the material that younger people want to ain't it?", "That's the material that younger people want. Ain't it?" ]
['that is the material the younger people want', 'that is the material the younger people want to aint it', 'that is the material the younger people want aint it', 'that is the material that younger people want to aint it', 'that is the material that younger people want aint 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.
well we discussed it in a previous meeting so i figured i will just leave it at the l c d
[ "Well, we discussed it in a previous meeting, so I figured I'll just leave it at the LCD.", "Well, we discussed it in a previous meeting, so I figured just leave it at the LCD.", "Well, we discussed it in a previous meeting, so I figured, just leave it at the LCD.", "Well, we discussed it in a previous meeting. So I figured I'll just leave it at the LCD.", "Well, we discussed it in a previous meeting. So I figured, just leave it at the LCD." ]
['well we discussed it in a previous meeting so i figured i will just leave it at the lcd', 'well we discussed it in a previous meeting so i figured just leave it at the lcd', 'well we discussed it in a previous meeting so i figured just leave it at the lcd', 'well we discussed it in a previous meeting so i figured i will just leave it at the lcd', 'well we discussed it in a previous meeting so i figured just leave it at the lcd']
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.
let us say we do and and well whatever cho child just goes up to the t v and presses up for instance
[ "And that's how we do. And well, what if a child a child just goes up to the TV and presses up for instance.", "And that's how we do. And well, what if a child a child just goes up to the TV and presses up, for instance.", "And that's how we do and well, what if a child a child just goes up to the TV and presses up for instance.", "And that's how we do. And well, what if a child, a child just goes up to the TV and presses up, for instance.", "And that's how we do it. And well, what if a child a child just goes up to the TV and presses up, for instance." ]
['and that is how we do and well what if a child a child just goes up to the tv and presses up for instance', 'and that is how we do and well what if a child a child just goes up to the tv and presses up for instance', 'and that is how we do and well what if a child a child just goes up to the tv and presses up for instance', 'and that is how we do and well what if a child a child just goes up to the tv and presses up for instance', 'and that is how we do it and well what if a child a child just goes up to the tv and presses up for instance']
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 have you considered the option of using a solar panel
[ "Um, Have you considered the option of using a solar panel?", "Have you considered the option of using a solar panel?", "Uh, Have you considered the option of using a solar panel?", "Um have you considered the option of using a solar panel?", "Um, have you considered the option of using a solar panel?" ]
['have you considered the option of using a solar panel', 'have you considered the option of using a solar panel', 'have you considered the option of using a solar panel', 'have you considered the option of using a solar panel', 'have you considered the option of using a solar panel']
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 talk about it later
[ "I'll talk about it later.", "Talk about it later.", "We'll talk about it later.", "talk about it later.", "I will talk about it later." ]
['i will talk about it later', 'talk about it later', 'we will talk about it later', 'talk about it later', 'i will talk about it later']
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 well you kn you all know the t v levels
[ "And well, you all know the TV levels.", "And well you all know the TV levels.", "And, well, you all know the TV levels.", "And well, you all know that TV levels.", "And well, you know, all know the TV levels." ]
['and well you all know the tv levels', 'and well you all know the tv levels', 'and well you all know the tv levels', 'and well you all know that tv levels', 'and well you know all know the tv levels']
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.
very special next session
[ "Very special next session.", "Very special, next session.", "Very special. Next session.", "Fairly special next session.", "Fairy special next session." ]
['very special next session', 'very special next session', 'very special next session', 'fairly special next session', 'fairy special next session']
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 al you also take t you take your ipac and go play games
[ "You also take you take your iPad and go playing games.", "You also take you take your iPad and go play games.", "You also take you take your iPack and go playing games.", "You also take you take your iPack and go play games.", "You also take, you take your iPad and go playing games." ]
['you also take you take your ipad and go playing games', 'you also take you take your ipad and go play games', 'you also take you take your ipack and go playing games', 'you also take you take your ipack and go play games', 'you also take you take your ipad and go playing games']
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.
acu yeah
[ "Acu.", "Acute.", "Acua.", "Acua, yeah.", "Acua. Yeah." ]
['acu', 'acute', 'acua', 'acua yeah', 'acua 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.
expert map
[ "Expert map.", "Expert Map.", "expert map.", "Expert map?", "Expert Map?" ]
['expert map', 'expert map', 'expert map', 'expert map', 'expert map']
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.
a combination yeah
[ "A combination, yeah.", "A combination.", "A combination. Yeah.", "The combination.", "In combination." ]
['a combination yeah', 'a combination', 'a combination yeah', 'the combination', 'in combination']
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 the games are in it
[ "And the games to are in it.", "And the games are in it.", "And the games too are in it.", "And the games still are in it.", "And the games, too, are in it." ]
['and the games to are in it', 'and the games are in it', 'and the games too are in it', 'and the games still are in it', 'and the games too are in 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.
yeah
[ "Yeah.", "Yes.", "Here.", "yeah.", "Yeah," ]
['yeah', 'yes', 'here', '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.
som some bench marker
[ "Some benchmarks.", "Some benchmark.", "some benchmarks.", "some benchmark.", "Some benchmarks?" ]
['some benchmarks', 'some benchmark', 'some benchmarks', 'some benchmark', 'some benchmarks']
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 here
[ "And here?", "And here.", "and here.", "and here?", "And here," ]
['and here', 'and here', 'and here', 'and here', 'and here']
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 it possible of is it necessary to make a touch screen square
[ "Is it possible, is it necessary to make a touch screen square?", "Is it possible is it necessary to make a touch screen square?", "Is it possible, is it necessary to make a touchscreen square?", "Is it possible is it necessary to make a touchscreen square?", "Is it possible of is it necessary to make a touch screen square?" ]
['is it possible is it necessary to make a touch screen square', 'is it possible is it necessary to make a touch screen square', 'is it possible is it necessary to make a touchscreen square', 'is it possible is it necessary to make a touchscreen square', 'is it possible of is it necessary to make a touch screen square']
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.
design
[ "Design.", "design.", "Design?", "design?", "The design." ]
['design', 'design', 'design', 'design', 'the design']
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 then then it d then it has some more energy
[ "And then then it then it adds some more energy.", "And then then it then it has some more energy.", "And then then then it has some more energy.", "Ah, then then it then it has some more energy.", "Ah, then then it then it adds some more energy." ]
['and then then it then it adds some more energy', 'and then then it then it has some more energy', 'and then then then it has some more energy', 'ah then then it then it has some more energy', 'ah then then it then it adds some more energy']
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.
real reaction remote
[ "Real reaction remote.", "Real reaction remote?", "real reaction remote.", "Real Reaction Remote.", "real reaction remote?" ]
['real reaction remote', 'real reaction remote', 'real reaction remote', 'real reaction remote', 'real reaction remote']
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 thought they were just
[ "I thought they were just.", "I thought they were just", "I thought they were just,", "I thought they were just?", "I thought there were just." ]
['i thought they were just', 'i thought they were just', 'i thought they were just', 'i thought they were just', 'i thought there were just']
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 parental control the games and the voice recognition
[ "The parental control, the games and the voice recognition.", "The parental control, the games, and the voice recognition.", "The parental control, the games and voice recognition.", "The parental control the games and the voice recognition.", "The parental control, the games, and voice recognition." ]
['the parental control the games and the voice recognition', 'the parental control the games and the voice recognition', 'the parental control the games and voice recognition', 'the parental control the games and the voice recognition', 'the parental control the games and voice recognition']
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.", "yeah.", "Yeah?" ]
['yeah', 'yeah', 'yes', '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.
i figured it will be m rather than hard nah rubber c this is a casing yeah
[ "I figured it would be rather hard. This is encasing.", "I figured it would be rather hard. No, rather this is encasing.", "I figured it would be rather hard. Now rather, this is encasing.", "I figured it would be rather hard. No, rather, this is encasing.", "I figured it would be rather hard. Now, rather, this is encasing." ]
['i figured it would be rather hard this is encasing', 'i figured it would be rather hard no rather this is encasing', 'i figured it would be rather hard now rather this is encasing', 'i figured it would be rather hard no rather this is encasing', 'i figured it would be rather hard now rather this is encasing']
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 this one
[ "Figures this one.", "I think it's this one.", "Figure's this one.", "I figure it's this one.", "I figured it was this one." ]
['figures this one', 'i think it is this one', 'figure is this one', 'i figure it is this one', 'i figured it was this 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.
if it is not manageable budget wise we would have to go over to to sim to simple buttons
[ "If it isn't manageable budget wise, we'd have to go off to two simple buttons.", "If it isn't manageable budget wise, we'd have to go off to some two simple buttons.", "If it isn't manageable budget wise, would have to go off to two simple buttons.", "If it isn't manageable budget wise, we'd have to go off to to some two simple buttons.", "If it isn't manageable budget wise, we'd have to go off to, to some two simple buttons." ]
['if it is not manageable budget wise we would have to go off to 2 simple buttons', 'if it is not manageable budget wise we would have to go off to some 2 simple buttons', 'if it is not manageable budget wise would have to go off to 2 simple buttons', 'if it is not manageable budget wise we would have to go off to to some 2 simple buttons', 'if it is not manageable budget wise we would have to go off to to some 2 simple buttons']
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 not sure if we we because i saw something about individual actions
[ "Yeah, I'm not sure if we because I saw something about individual actions.", "Yeah, I'm not sure if we, because I saw something about individual actions.", "Yeah, I'm not sure if we are we because I saw something about individual actions.", "Yeah, I'm not sure if we are, because I saw something about individual actions.", "Yeah, I'm not sure if we're we because I saw something about individual actions." ]
['yeah i am not sure if we because i saw something about individual actions', 'yeah i am not sure if we because i saw something about individual actions', 'yeah i am not sure if we are we because i saw something about individual actions', 'yeah i am not sure if we are because i saw something about individual actions', 'yeah i am not sure if we are we because i saw something about individual actions']
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
[ "Like.", "Like a.", "like.", "Like, uh.", "Like uh." ]
['like', 'like a', 'like', 'like', 'like']
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.