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Crowd-sourcing for difficult transcription of speech
Jason Williams, Dan Melamed, Tirso Alonso, Barbara Hollister, Jay Wilpon
IEEE Workshop on Automatic Speech Recognition and Understanding, Hawaii, USA,
IEEE Workshop on Automatic Speech Recognition and Understanding,
2011.
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IEEE Copyright
This version of the work is reprinted here with permission of IEEE for your personal use. Not for redistribution. The definitive version was published in IEEE Workshop on Automatic Speech Recognition and Understanding. , 2011-12-11
Crowd-sourcing is a promising method for fast and cheap transcription of large volumes of speech data. However, this method cannot achieve the accuracy of expert transcribers on speech that is difficult to transcribe. Faced with such speech data, we developed three new methods of crowd-sourcing allow explicit trade-offs among precision, recall, and cost. The methods are: incremental crowd-sourcing, treating ASR as a transcriber, and using a regression model to predict transcription reliability. Even though the accuracy of individual crowd-workers is only 55% on our data, our best method achieves 90% accuracy on 93% of the utterances, using only 1.3 crowd-worker transcriptions per utterance on average. When forced to transcribe all utterances, our best method matches the accuracy of previous crowdsourcing methods using only one third as many transcriptions. We also study the effects of various task design factors on transcription latency and accuracy, some of which have not been studied before.
Voice-Enabled Dialog System,
Tue Jan 11 16:04:22 EST 2011
A voice-enabled help desk service is disclosed. The service comprises an automatic speech recognition module for recognizing speech from a user, a spoken language understanding module for understanding the output from the automatic speech recognition module, a dialog management module for generating a response to speech from the user, a natural voices text-to-speech synthesis module for synthesizing speech to generate the response to the user, and a frequently asked questions module. The frequently asked questions module handles frequently asked questions from the user by changing voices and providing predetermined prompts to answer the frequently asked question.
Method Of Generation A Labeling Guide For Spoken Dialog Services,
Tue Jun 01 15:03:58 EDT 2010
A method is disclosed for designing a labeling guide for use by a labeler in labeling data used for training a spoken language understanding (SLU) module for an application. The method comprises a labeling guide designer selecting domain-independent actions applicable to an application, selecting domain-dependent objects according to characteristics of the application, and generating a labeling guide using the selected domain-independent actions and selected domain-dependent objects. An advantage of the labeling guide generated in this manner is that the labeling guide designer can easily port the labeling guide to a new application by selecting a set of domain-independent action and then selecting the domain-dependent objects related to the new application.
Reducing time for annotating speech data to develop a dialog application,
Tue Aug 12 18:12:58 EDT 2008
Systems and methods for annotating speech data. The present invention reduces the time required to annotate speech data by selecting utterances for annotation that will be of greatest benefit. A selection module uses speech models, including speech recognition models and spoken language understanding models, to identify utterances that should be annotated based on criteria such as confidence scores generated by the models. These utterances are placed in an annotation list along with a type of annotation to be performed for the utterances and an order in which the annotation should proceed. The utterances in the annotation list can be annotated for speech recognition purposes, spoken language understanding purposes, labeling purposes, etc. The selection module can also select utterances for annotation based on previously annotated speech data and deficiencies in the various models.
Method of generating a labeling guide for spoken dialog services,
Tue Apr 29 18:12:46 EDT 2008
A method is disclosed for designing a labeling guide for use by a labeler in labeling data used for training a spoken language understanding (SLU) module for an application. The method comprises a labeling guide designer selecting domain-independent actions applicable to an application, selecting domain-dependent objects according to characteristics of the application, and generating a labeling guide using the selected domain-independent actions and selected domain-dependent objects. An advantage of the labeling guide generated in this manner is that the labeling guide designer can easily port the labeling guide to a new application by selecting a set of domain-independent action and then selecting the domain-dependent objects related to the new application.
