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so that should not be a problem
[ "So that shouldn't be a problem.", "So, that shouldn't be a problem.", "so that shouldn't be a problem.", "So that shouldn't be a problem?", "So that shouldn't be a problem," ]
['so that should not be a problem', 'so that should not be a problem', 'so that should not be a problem', 'so that should not be a problem', 'so that should not be a 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.
but you prefer kinetic
[ "Would you prefer kinetic?", "Would you prefer a kinetic?", "Would you prefer Kinetic?", "Would you prefer kinetic.", "Would you prefer kinetics?" ]
['would you prefer kinetic', 'would you prefer a kinetic', 'would you prefer kinetic', 'would you prefer kinetic', 'would you prefer kinetics']
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.
any ideas
[ "Kenny Idish.", "Can you add this?", "Can you idish?", "Henry Idish.", "Kennedy Idish." ]
['kenny idish', 'can you add this', 'can you idish', 'henry idish', 'kennedy idish']
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.
maybe it is handy to build in an expert view and a simple view
[ "Maybe it's handy to build in an expert view and a simple view.", "Maybe it's uh handy to build in an expert view and a simple view.", "Maybe it's, uh, handy to build in an expert view and a simple view.", "Maybe it's uh, handy to build in an expert view and a simple view.", "Maybe it's a handy to build in an expert view and a simple view." ]
['maybe it is handy to build in an expert view and a simple view', 'maybe it is handy to build in an expert view and a simple view', 'maybe it is handy to build in an expert view and a simple view', 'maybe it is handy to build in an expert view and a simple view', 'maybe it is a handy to build in an expert view and a simple view']
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.", "Oh, okay.", "Okay,", "OK." ]
['okay', 'ok', '0 okay', 'okay', 'ok']
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 project manager is problem
[ "That's the project manager's problem.", "That's the project managers problem.", "That's the project management problem.", "That's the Project Manager's problem.", "That's the project managers' problem." ]
['that is the project manager is problem', 'that is the project managers problem', 'that is the project management problem', 'that is the project manager is problem', 'that is the project managers 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.
rubber
[ "Rubber.", "rubber.", "Rubber?", "Rubber...", "rubber?" ]
['rubber', 'rubber', 'rubber', 'rubber .', 'rubber']
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 solve the problem
[ "That would solve the problem.", "That that would solve the problem.", "That, that would solve the problem.", "That would solve problem.", "that would solve the problem." ]
['that would solve the problem', 'that that would solve the problem', 'that that would solve the problem', 'that would solve problem', 'that would solve 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.
so stupid manager
[ "So stupid manager.", "So, stupid manager.", "so stupid manager.", "So. Stupid manager.", "So, Stupid manager." ]
['so stupid manager', 'so stupid manager', 'so stupid manager', 'so stupid manager', 'so stupid manager']
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 not possible to make combination with kind of rubber is or bendable remotes where you have got a
[ "Isn't it possible to make combination? What kind of rubber is it all bendable removed so you gotta sit there around?", "Isn't it possible to make combination? What kind of rubber is is all bendable removed so you gotta sit there around?", "Isn't it possible to make combination? What kind of rubber? Is it all bendable removed so you gotta sit there around?", "Isn't it possible to make combination? What kind of rubber? Is it all bendable removed so you've got to sit there around?", "Isn't it possible to make combination? What kind of rubber? Is it all bendable removed? So you gotta sit there around." ]
['is not it possible to make combination what kind of rubber is it all bendable removed so you got to sit there around', 'is not it possible to make combination what kind of rubber is is all bendable removed so you got to sit there around', 'is not it possible to make combination what kind of rubber is it all bendable removed so you got to sit there around', 'is not it possible to make combination what kind of rubber is it all bendable removed so you have got to sit there around', 'is not it possible to make combination what kind of rubber is it all bendable removed so you got to sit there around']
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.", "Oh, okay.", "Oh okay.", "Ok.", "Oh, Okay." ]
['okay', '0 okay', '0 okay', 'ok', '0 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.
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.
5 minutes to go
[ "five minutes to go.", "Five minutes to go.", "It's five minutes to go.", "five minutes to go", "five minutes to go?" ]
['5 minutes to go', '5 minutes to go', 'it is 5 minutes to go', '5 minutes to go', '5 minutes to 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.
rubber or plastic
[ "a rubber or plastic.", "a rubber or plastic?", "rubber or plastic.", "a rubber or plastic", "in a rubber or plastic." ]
['a rubber or plastic', 'a rubber or plastic', 'rubber or plastic', 'a rubber or plastic', 'in a rubber or plastic']
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.
w
[ "Oh.", "Oh. What?", "Oh, what?", "Oh. What? Go on.", "Oh. What? Come on." ]
['0', '0 what', '0 what', '0 what go on', '0 what come on']
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 guys lot of success
[ "Okay guys, love success.", "Okay, guys. Love Success.", "Okay guys, loud success.", "Okay, guys, love success.", "Okay guys. Love Success." ]
['okay guys love success', 'okay guys love success', 'okay guys loud success', 'okay guys love success', 'okay guys love success']
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.
would you prefer that you can choose the color of your remote control or
[ "Would you prefer that you can choose the color of your remote control or?", "Would you prefer that you can choose the color of your remote control, or?", "Would you prefer that you can choose the color of your remote control?", "Would you prefer that you can choose the color of your remote control or.", "Would you prefer that you can choose the colour of your remote control or?" ]
['would you prefer that you can choose the color of your remote control or', 'would you prefer that you can choose the color of your remote control or', 'would you prefer that you can choose the color of your remote control', 'would you prefer that you can choose the color of your remote control or', 'would you prefer that you can choose the color of your remote control or']
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 okay
[ "Yeah, Okay.", "Yeah okay.", "Yeah, okay.", "Yeah, OK.", "Yeah. Okay." ]
['yeah okay', 'yeah okay', 'yeah okay', 'yeah ok', 'yeah 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.
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.
yeah
[ "Well.", "Well,", "Well, yeah.", "Wow.", "well," ]
['well', 'well', 'well yeah', 'wow', '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.
yeah j just some rules
[ "And just some rules.", "Yeah. Just some rules.", "Just some rules.", "Yeah, just some rules.", "Yeah, Ju just some rules." ]
['and just some rules', 'yeah just some rules', 'just some rules', 'yeah just some rules', 'yeah ju just some rules']
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.", "No.", "Nah.", "Yeah,", "Yes." ]
['yeah', 'no', 'nah', '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.
project manager
[ "Project Manager.", "Project manager.", "project manager.", "Project manager?", "Project Manager?" ]
['project manager', 'project manager', 'project manager', 'project manager', 'project manager']
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 it has to be fast
[ "It has to be fast.", "It it has to be fast.", "It, it has to be fast.", "it has to be fast.", "It has to be fast" ]
['it has to be fast', 'it it has to be fast', 'it it has to be fast', 'it has to be fast', 'it has to be fast']
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 so what are the opinions
[ "So what are the opinions?", "So, so what are the opinions?", "So also what are the opinions?", "So, also what are the opinions?", "So, so, so what are the opinions?" ]
['so what are the opinions', 'so so what are the opinions', 'so also what are the opinions', 'so also what are the opinions', 'so so so what are the opinions']
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.
your project manager
[ "Your project manager.", "your project manager.", "your project manager", "your project manager,", "Your project manager?" ]
['your project manager', 'your project manager', 'your project manager', 'your project manager', 'your project manager']
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.
master
[ "Master.", "Master", "Maste.", "master.", "Maastricht." ]
['master', 'master', 'maste', 'master', 'maastricht']
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 w it just has to be w when it is deliver out of the store it has to be just simple and plain
[ "Yeah, it just has to be when it's delivered out of the store. It has to be just simple and plain.", "Yeah, it just has to be when it's delivered out of the store, it has to be just simple and plain.", "Yeah, it just has to be when it's delivered out of the store it has to be just simple and plain.", "Yeah. It just has to be when it's delivered out of the store. It has to be just simple and plain.", "Yeah, it just has to be, when it's delivered out of the store, it has to be just simple and plain." ]
['yeah it just has to be when it is delivered out of the store it has to be just simple and plain', 'yeah it just has to be when it is delivered out of the store it has to be just simple and plain', 'yeah it just has to be when it is delivered out of the store it has to be just simple and plain', 'yeah it just has to be when it is delivered out of the store it has to be just simple and plain', 'yeah it just has to be when it is delivered out of the store it has to be just simple and plain']
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 how can we d aim a at the target group
[ "And how can we aim at the target group?", "And how can we aim at the target group.", "How can we aim at the target group?", "and how can we aim at the target group?", "And how can we, uh, aim at the target group?" ]
['and how can we aim at the target group', 'and how can we aim at the target group', 'how can we aim at the target group', 'and how can we aim at the target group', 'and how can we aim at the target group']
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 the link with mobile phones
[ "Also the link with mobile phones.", "Also the link with the mobile phones.", "Also, the link with mobile phones.", "Also, the link with the mobile phones.", "Uh, also the link with the mobile phones." ]
['also the link with mobile phones', 'also the link with the mobile phones', 'also the link with mobile phones', 'also the link with the mobile phones', 'also the link with the mobile phones']
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 i still believe you should keep displaying the buttons
[ "And well, I still believe you should keep displaying the buttons.", "And well I still believe you should keep displaying the buttons.", "And well, I still believe you should keep displaying the buttons,", "And well, i still believe you should keep displaying the buttons.", "And, Well, I still believe you should keep displaying the buttons." ]
['and well i still believe you should keep displaying the buttons', 'and well i still believe you should keep displaying the buttons', 'and well i still believe you should keep displaying the buttons', 'and well i still believe you should keep displaying the buttons', 'and well i still believe you should keep displaying the 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.
not in the master class
[ "Not in a master class.", "Not in a masterclass.", "Not in the master class.", "Not in a Masterclass.", "Not in the Masterclass." ]
['not in a master class', 'not in a masterclass', 'not in the master class', 'not in a masterclass', 'not in 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.
yeah that is true
[ "Yeah, that is true.", "Yeah that is true.", "Yeah. That is true.", "Yes, that is true.", "Yeah, that's true." ]
['yeah that is true', 'yeah that is true', 'yeah that is true', 'yes that is true', 'yeah that is true']
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 we have we have decided okay
[ "I think we have decided, okay?", "I think we have decided okay.", "I think we have decided, okay.", "I think we have decided, OK?", "I think we have, we have decided, okay?" ]
['i think we have decided okay', 'i think we have decided okay', 'i think we have decided okay', 'i think we have decided ok', 'i think we have we have decided 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.
and if you are using that button a lot of times well of course the menu will still be displayed on the screen
[ "And if you're using that button a lot of times, well, of course, that menu will still be displayed on screen.", "And if you're using that button a lot of times, well of course that menu will still be displayed on screen.", "And if you're using that button a lot of times, well, of course that menu will still be displayed on screen.", "And if you're using that button a lot of times, well, of course, that menu will still be displayed on the screen.", "And if you are using that button a lot of times, well, of course, that menu will still be displayed on screen." ]
['and if you are using that button a lot of times well of course that menu will still be displayed on screen', 'and if you are using that button a lot of times well of course that menu will still be displayed on screen', 'and if you are using that button a lot of times well of course that menu will still be displayed on screen', 'and if you are using that button a lot of times well of course that menu will still be displayed on the screen', 'and if you are using that button a lot of times well of course that menu will still be displayed on screen']
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 for rest you would just add juice on the battery
[ "And for rest you just juice on the battery.", "And for rest, you just juice on the battery.", "And for rest you just juice on a battery.", "And for rest, you just juice on a battery.", "And for us, you just juice on the battery." ]
['and for rest you just juice on the battery', 'and for rest you just juice on the battery', 'and for rest you just juice on a battery', 'and for rest you just juice on a battery', 'and for us you just juice on the battery']
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?", "Okay, okay.", "OK, OK." ]
['okay', 'ok', 'okay', 'okay okay', 'ok ok']
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 you just wait for the specific instructions and but i think it was a very very good session gentlemen
[ "So you just wait for the specific instructions and but I think it was very a very good session gentlemen.", "So you just wait for the specific instructions and but I think it was very a very good session, gentlemen.", "So you just wait for the specific instructions and but I think it was a very a very good session gentlemen.", "So you just wait for the specific instructions and but I think it was a very a very good session, gentlemen.", "So you just wait for the specific instructions. But I think it was a very, very good session, gentlemen." ]
['so you just wait for the specific instructions and but i think it was very a very good session gentlemen', 'so you just wait for the specific instructions and but i think it was very a very good session gentlemen', 'so you just wait for the specific instructions and but i think it was a very a very good session gentlemen', 'so you just wait for the specific instructions and but i think it was a very a very good session gentlemen', 'so you just wait for the specific instructions but i think it was a very very good session gentlemen']
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.
in paris and milan we asked different people differing in age and in income
[ "In Paris and Milan, we asked different people, differing in age and in income.", "In Paris and Milan, we asked different people differing in age and in income.", "In Paris and Milan we asked different people, differing in age and in income.", "In Paris and Milan, we asked different people, uh, differing in age and in income.", "In Paris and Milan, we, uh, asked different people, uh, differing in age and in income." ]
['in paris and milan we asked different people differing in age and in income', 'in paris and milan we asked different people differing in age and in income', 'in paris and milan we asked different people differing in age and in income', 'in paris and milan we asked different people differing in age and in income', 'in paris and milan we asked different people differing in age and in income']
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.
seen on regular mobile phones
[ "Seen on regular mobile phones.", "seen on regular mobile phones.", "Seen on regular mobile phones?", "Seeing on regular mobile phones.", "Seen on regular mobile phones." ]
['seen on regular mobile phones', 'seen on regular mobile phones', 'seen on regular mobile phones', 'seeing on regular mobile phones', 'seen on regular mobile phones']
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 we have to keep the cost in in mind but it
[ "But we have to keep the cost in mind, but it", "But we have to keep the cost in mind, but it.", "But we have to keep the cost in in mind, but it", "Well, we have to keep the cost in mind, but it.", "But we have to keep the cost in, in mind, but it" ]
['but we have to keep the cost in mind but it', 'but we have to keep the cost in mind but it', 'but we have to keep the cost in in mind but it', 'well we have to keep the cost in mind but it', 'but we have to keep the cost in in mind but 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 you could y it leaves more space for creativity
[ "Yeah, you could at least more space for creativity.", "Yeah you could at least more space for creativity.", "Yeah, you could, at least more space for creativity.", "Yeah. You could at least more space for creativity.", "Yeah you could, at least more space for creativity." ]
['yeah you could at least more space for creativity', 'yeah you could at least more space for creativity', 'yeah you could at least more space for creativity', 'yeah you could at least more space for creativity', 'yeah you could at least more space for creativity']
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.
why
[ "Why?", "Why you?", "Well, you", "Why you", "Well you." ]
['why', 'why you', 'well you', 'why you', 'well 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.
me too
[ "Me too.", "me too.", "Me, too.", "I too.", "Me too..." ]
['me too', 'me too', 'me too', 'i too', 'me 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.", "Yes.", "yeah.", "Yeah, yeah.", "Yeah," ]
['yeah', 'yes', 'yeah', 'yeah 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.
to log in
[ "to log in.", "To log in.", "to login.", "To login.", "to log in," ]
['to log in', 'to log in', 'to login', 'to login', 'to log in']
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 make it ourselves very diffic
[ "Or make it ourselves very different.", "or make it ourselves very different.", "We make it ourselves very different.", "Or make it ourselves very difficult.", "we make it ourselves very different." ]
['or make it ourselves very different', 'or make it ourselves very different', 'we make it ourselves very different', 'or make it ourselves very difficult', 'we make it ourselves very different']
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 could you could you could go like that that would actually make things a lot more easy
[ "Yeah, you could you could you could go like that that would actually make things a lot more easy.", "Yeah, you could, you could, you could go like that. Yeah, that, that would actually make things a lot more easy.", "Yeah, you could, you could, you could go like that. Yeah, that that would actually make things a lot more easy.", "Yeah, you could, you could, you could go like that, that would actually make things a lot more easy.", "Yeah, you could, you could, you could go like that. That, that would actually make things a lot more easy." ]
['yeah you could you could you could go like that that would actually make things a lot more easy', 'yeah you could you could you could go like that yeah that that would actually make things a lot more easy', 'yeah you could you could you could go like that yeah that that would actually make things a lot more easy', 'yeah you could you could you could go like that that would actually make things a lot more easy', 'yeah you could you could you could go like that that that would actually make things a lot more easy']
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', 'yeah', 'yes', 'yeah 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.
they should be technological in innovative
[ "They should be technological innovative.", "Um, they should be technological innovative.", "They should be technological in innovative.", "Um, They should be technological innovative.", "They should be technological and innovative." ]
['they should be technological innovative', 'they should be technological innovative', 'they should be technological in innovative', 'they should be technological innovative', 'they should be technological and innovative']
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 on the t v
[ "But on the TV.", "But on the TV?", "But on the TV", "but on the TV.", "But on the Tv." ]
['but on the tv', 'but on the tv', 'but on the tv', 'but on the tv', 'but on the tv']
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.
user profile
[ "Use a profile.", "Use your profile.", "User profile.", "Use the profile.", "User Profile." ]
['use a profile', 'use your profile', 'user profile', 'use the profile', 'user profile']
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 nob nobody is stopping the child from well checking the channel
[ "Well, nobody is stopping the child from checking the channel.", "Well nobody is stopping the child from checking the channel.", "Well, nobody's stopping the child from checking the channel.", "Well, nobody is stopping a child from checking the channel.", "Well, no, nobody is stopping the child from checking the channel." ]
['well nobody is stopping the child from checking the channel', 'well nobody is stopping the child from checking the channel', 'well nobody is stopping the child from checking the channel', 'well nobody is stopping a child from checking the channel', 'well no nobody is stopping the child from checking the channel']
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.", "Ya." ]
['yeah', 'yes', 'yeah', 'yeah yeah', 'ya']
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.
combine them
[ "Combine them.", "combine them.", "Combine them?", "Combined them.", "Combine them." ]
['combine them', 'combine them', 'combine them', 'combined them', 'combine 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.
but maybe there is some option that that t the kind of show view numbers are violent and that they are blocked out
[ "But maybe there's some option that the kind of show view numbers are violent and that they are blocked out.", "But maybe there's some option that that the kind of show view numbers are violent and that they are blocked out.", "But maybe there's some option that, uh, that the kind of show view numbers are violent and that they are blocked out.", "But maybe there's some option that uh that the kind of show view numbers are violent and that they are blocked out.", "But maybe there's some option that, uh, that the kind of show few numbers are violent and that they are blocked out." ]
['but maybe there is some option that the kind of show view numbers are violent and that they are blocked out', 'but maybe there is some option that that the kind of show view numbers are violent and that they are blocked out', 'but maybe there is some option that that the kind of show view numbers are violent and that they are blocked out', 'but maybe there is some option that that the kind of show view numbers are violent and that they are blocked out', 'but maybe there is some option that that the kind of show few numbers are violent and that they are blocked out']
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 only for maybe after 100 clock in the evening violent films and movies will come and maybe maybe some some timing will be needed instead of of channels
[ "And only maybe after ten o'clock in the evening violent films and movies will come and maybe some timing will be needed instead of channels.", "Only maybe after ten o'clock in the evening violent films and movies will come and maybe some timing will be needed instead of channels.", "And only maybe after ten o'clock in the evening, violent films and movies will come and maybe some timing will be needed instead of channels.", "And only, maybe after ten o'clock in the evening, violent films and movies will come and maybe some timing will be needed instead of channels.", "Only, maybe after ten o'clock in the evening, violent films and movies will come and maybe some timing will be needed instead of channels." ]
['and only maybe after 100 clock in the evening violent films and movies will come and maybe some timing will be needed instead of channels', 'only maybe after 100 clock in the evening violent films and movies will come and maybe some timing will be needed instead of channels', 'and only maybe after 100 clock in the evening violent films and movies will come and maybe some timing will be needed instead of channels', 'and only maybe after 100 clock in the evening violent films and movies will come and maybe some timing will be needed instead of channels', 'only maybe after 100 clock in the evening violent films and movies will come and maybe some timing will be needed instead of channels']
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 okay
[ "Okay.", "Oh, okay.", "OK.", "Ok.", "Ah, okay." ]
['okay', '0 okay', 'ok', 'ok', 'ah 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.
yeah
[ "Yeah.", "Yes.", "Yeah, yeah.", "yeah.", "Yeah," ]
['yeah', 'yes', 'yeah yeah', '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.
yeah
[ "Yeah.", "Hm.", "Yum.", "Hmm.", "Mm." ]
['yeah', 'hm', 'yum', '', '']
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.", "Mkay.", "Mmkay.", "Ok.", "Mm." ]
['okay', 'mkay', 'mmkay', 'ok', '']
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, yeah." ]
['yeah', 'yes', 'yeah', 'yeah', '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.
yeah
[ "Yeah.", "yeah.", "Yeah,", "Yes.", "yeah," ]
['yeah', 'yeah', 'yeah', '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.
i had a i had another idea to put the whole the whole idea of real reaction and a single brand and that kind of thing
[ "I had another idea to put the whole idea of real reaction and a single brunt and that kind of thing.", "I had another idea to put the whole idea of a real reaction and a single brunt and that kind of thing.", "I had another idea to put the whole idea of real reaction and a single brand and that kind of thing.", "I had another idea to put the whole idea of a real reaction and a single brand and that kind of thing.", "I had another idea to put, the whole idea of a real reaction and a single brunt and that kind of thing." ]
['i had another idea to put the whole idea of real reaction and a single brunt and that kind of thing', 'i had another idea to put the whole idea of a real reaction and a single brunt and that kind of thing', 'i had another idea to put the whole idea of real reaction and a single brand and that kind of thing', 'i had another idea to put the whole idea of a real reaction and a single brand and that kind of thing', 'i had another idea to put the whole idea of a real reaction and a single brunt and that kind of 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.
little more
[ "Uh, Little More.", "A little more.", "Uh, little more.", "Uh little more.", "Uh Little More." ]
['little more', 'a little more', 'little more', 'little more', 'little more']
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.
he is the master yeah
[ "He's the monster.", "He's the monster. Yeah.", "He's the monster, yeah.", "He's the monster. Yeah. Yeah, just.", "He's the monster. Yeah. Yeah, that is." ]
['he is the monster', 'he is the monster yeah', 'he is the monster yeah', 'he is the monster yeah yeah just', 'he is the monster yeah yeah that 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.
but keep an eye on your laptops for a real
[ "But keep an eye on your laptop for a real.", "But keep an eye on your laptop for real.", "But keep an eye on your laptop for a real", "but keep an eye on your laptop for a real.", "But keep an eye on your laptop for a real," ]
['but keep an eye on your laptop for a real', 'but keep an eye on your laptop for real', 'but keep an eye on your laptop for a real', 'but keep an eye on your laptop for a real', 'but keep an eye on your laptop for a real']
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.
think of the teletubbies for instance
[ "Think of the teletubbies, for instance.", "Think of the teletubbies for instance.", "Think of the telitubbies, for instance.", "Think of the telitubbies for instance.", "Think of the Teletubbies, for instance." ]
['think of the teletubbies for instance', 'think of the teletubbies for instance', 'think of the telitubbies for instance', 'think of the telitubbies for instance', 'think of the teletubbies 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.
but that is our secondary audience
[ "But that's our secondary audience.", "But, that's our secondary audience.", "But, That's our secondary audience.", "But that is our secondary audience.", "But that's our secondary audience" ]
['but that is our secondary audience', 'but that is our secondary audience', 'but that is our secondary audience', 'but that is our secondary audience', 'but that is our secondary audience']
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 i think that touch screens are generally square
[ "Well, I think touch screens are generally square.", "Well I think touch screens are generally square.", "Well, I think that touch screens are generally square.", "Well I think that touch screens are generally square.", "Well, I think that that screens are generally square." ]
['well i think touch screens are generally square', 'well i think touch screens are generally square', 'well i think that touch screens are generally square', 'well i think that touch screens are generally square', 'well i think that that screens are generally 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.
here
[ "year.", "Year.", "Here.", "here.", "Here, year." ]
['year', 'year', 'here', 'here', 'here year']
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, yeah. So.", "Yeah, yeah, yeah. So.", "Yeah, yeah, yeah.", "Yeah, yeah, so.", "Yeah, yeah, yeah, yeah." ]
['yeah yeah so', 'yeah yeah yeah so', 'yeah yeah yeah', 'yeah yeah so', 'yeah yeah 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.
yeah
[ "Yeah.", "No.", "Yes.", "Yeah,", "Oh." ]
['yeah', 'no', 'yes', 'yeah', '0']
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 the teletubbies sh
[ "Teletubbish.", "Not a teletubbish.", "The Teletubbish.", "Not a teletubbish in there.", "Not a teletubbish, you know." ]
['teletubbish', 'not a teletubbish', 'the teletubbish', 'not a teletubbish in there', 'not a teletubbish 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 exactly
[ "Yeah, exactly.", "Yeah exactly.", "Exactly.", "Yeah. Exactly.", "Yeah, Exactly." ]
['yeah exactly', 'yeah exactly', 'exactly', 'yeah exactly', 'yeah exactly']
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 you could go for plastic but i figured
[ "So you could go for plastic, but I figured.", "So you could go for plastic but I figured.", "So, you could go for plastic, but I figured.", "John, you could go for plastic, but I figured.", "So you could go for plastic, but I figured," ]
['so you could go for plastic but i figured', 'so you could go for plastic but i figured', 'so you could go for plastic but i figured', 'john you could go for plastic but i figured', 'so you could go for plastic but i figured']
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 down here
[ "And down here.", "and down here.", "And down here,", "and down here,", "And, Down here." ]
['and down here', 'and down here', 'and down here', 'and down here', 'and down 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.
yeah
[ "Yeah.", "Yes.", "yeah.", "Yeah,", "Yes," ]
['yeah', 'yes', 'yeah', '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.
during lunch yeah
[ "During lunch.", "During lunch, yeah.", "During lunch yeah.", "during lunch.", "During lunch?" ]
['during lunch', 'during lunch yeah', 'during lunch yeah', 'during lunch', 'during lunch']
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
[ "Yes.", "Yes,", "Yeah.", "yes.", "Yes?" ]
['yes', 'yes', 'yeah', 'yes', '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.
yeah with and one without
[ "Yeah, with and one without.", "Yeah with and one without.", "Yeah, With and one without.", "Yeah. With and one without.", "Yeah, Within one without." ]
['yeah with and one without', 'yeah with and one without', 'yeah with and one without', 'yeah with and one without', 'yeah within one without']
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 a little problem big problem
[ "A little problem, big problem.", "A little problem. Big problem.", "No, a little problem. Big problem.", "No, little problem, big problem.", "No, a little problem, big problem." ]
['a little problem big problem', 'a little problem big problem', 'no a little problem big problem', 'no little problem big problem', 'no a little problem big 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.
and if you puts a ye
[ "And if you put", "And if he puts.", "And if you put.", "And if he puts", "And if he puts," ]
['and if you put', 'and if he puts', 'and if you put', 'and if he puts', 'and if he puts']
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.
something like that
[ "Something like that.", "something like that.", "Something like that?", "something like that?", "something like that," ]
['something like that', 'something like that', 'something like that', 'something like that', '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.
to customize your own remote control
[ "Customize your own remote control.", "To customize your own remote control.", "to customize your own remote control.", "customize your own remote control.", "Customize your own remote control?" ]
['customize your own remote control', 'to customize your own remote control', 'to customize your own remote control', 'customize your own remote control', 'customize your own remote control']
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.
yeah
[ "Yup.", "Yeah.", "Yep.", "Ya.", "yeah." ]
['yup', 'yeah', 'yep', 'ya', '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.
and what do you guys think of the games in the voice recognition
[ "And what do you guys think of the games and the voice recognition?", "And what do you guys think of the games in the voice recognition?", "And what do you guys think of the games and voice recognition?", "And what do you guys think of the games and the voice recognition.", "And what do you, uh, guys think of the games and the voice recognition?" ]
['and what do you guys think of the games and the voice recognition', 'and what do you guys think of the games in the voice recognition', 'and what do you guys think of the games and voice recognition', 'and what do you guys think of the games and the voice recognition', 'and what do you guys think of the games and the 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.
and and you know w when you install another device
[ "And you know when you install another device.", "And you know, when you install another device,", "And you know, when you install another device.", "And you know when you install another device,", "And, and you know, when you install another device," ]
['and you know when you install another device', 'and you know when you install another device', 'and you know when you install another device', 'and you know when you install another device', 'and and you know when you install another device']
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
[ "That.", "Yeah.", "that.", "Yep.", "That's right." ]
['that', 'yeah', 'that', 'yep', '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.
just keep it as simple as as possible
[ "Just keep it as simple as possible.", "Just keep it as simple as as possible.", "just keep it as simple as possible.", "Just keep it the simple as possible.", "I'll just keep it as simple as possible." ]
['just keep it as simple as possible', 'just keep it as simple as as possible', 'just keep it as simple as possible', 'just keep it the simple as possible', 'i will just keep it as simple as possible']
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 more like it gets you to the functionality but
[ "It's more like a gadget of functionality, but", "It's more like a gadget of functionality with.", "It's more like a gadget of functionality but", "It's more like a gadget of functionality with", "It's more like a gadget of functionality, but." ]
['it is more like a gadget of functionality but', 'it is more like a gadget of functionality with', 'it is more like a gadget of functionality but', 'it is more like a gadget of functionality with', 'it is more like a gadget of functionality but']
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 maybe use your thumb
[ "And maybe use your thumb.", "and maybe use your thumb.", "And maybe you should thumb.", "And maybe use your thumb?", "And maybe use your Thumb." ]
['and maybe use your thumb', 'and maybe use your thumb', 'and maybe you should thumb', 'and maybe use your thumb', 'and maybe use your thumb']
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 of course
[ "Yeah, of course.", "Yeah of course.", "Yeah, Of course.", "Yes, of course.", "Yeah. Of course." ]
['yeah of course', 'yeah of course', 'yeah of course', 'yes of course', 'yeah of course']
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.
take it
[ "Yeah.", "Thank you.", "Take my back.", "Take my back and call it a plate.", "Take my back and call it a place." ]
['yeah', 'thank you', 'take my back', 'take my back and call it a plate', 'take my back and call it a place']
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.", "Yea." ]
['yeah', 'yeah', 'yeah', 'yes', '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.
both
[ "Both.", "both.", "both?", "Both?", "Both..." ]
['both', 'both', 'both', 'both', '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.
me too
[ "Me too.", "Me, too.", "me too.", "Me to.", "Me too, yeah." ]
['me too', 'me too', 'me too', 'me to', 'me too 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.
that
[ "That's.", "that's", "That's,", "That's...", "That's, that's." ]
['that is', 'that is', 'that is', 'that is .', 'that is that 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.
we are we put fashion in electronics so maybe we can
[ "We put fashion electronics, so maybe we can.", "We put fashion electronics so maybe we can.", "We put fashion electronics so maybe we can", "We put fashion electronics. So maybe we can.", "We put fashion electronics, so maybe we can" ]
['we put fashion electronics so maybe we can', 'we put fashion electronics so maybe we can', 'we put fashion electronics so maybe we can', 'we put fashion electronics so maybe we can', 'we put fashion electronics so maybe we can']
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.