Minimizing annotation effort for adaptation of speech-activity detection systems
Annotating audio data for the presence and location of speech is a time-consuming and therefore costly task. This is mostly because annotation precision greatly affects the performance of the speech-activity detection (SAD) systems trained with this data, which means that the annotation process must...
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| Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_2308457X_v08-12-September-2016_n_p3002_Ferrer |
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todo:paper_2308457X_v08-12-September-2016_n_p3002_Ferrer2023-10-03T16:40:51Z Minimizing annotation effort for adaptation of speech-activity detection systems Ferrer, L. Graciarena, M. Morgan N. Georgiou P. Morgan N. Narayanan S. Metze F. Amazon Alexa; Apple; eBay; et al.; Google; Microsoft Active learning Adaptation Annotation Speech-activity detection Artificial intelligence Budget control Speech Speech communication Speech processing Active Learning Adaptation Annotation Audio samples Baseline systems Simple approach Speech activity detections Training data Speech recognition Annotating audio data for the presence and location of speech is a time-consuming and therefore costly task. This is mostly because annotation precision greatly affects the performance of the speech-activity detection (SAD) systems trained with this data, which means that the annotation process must be careful and detailed. Although significant amounts of data are already annotated for speech presence and are available to train SAD systems, these systems are known to perform poorly on channels that are not well-represented by the training data. However obtaining representative audio samples from a new channel is relative easy and this data can be used for training a new SAD system or adapting one trained with larger amounts of mismatched data. This paper focuses on the problem of selecting the best-possible subset of available audio data given a budgeted time for annotation. We propose simple approaches for selection that lead to significant gains over na?ive methods that merely select N full files at random. An approach that uses the framelevel scores from a baseline system to select regions such that the score distribution is uniformly sampled gives the best tradeoff across a variety of channel groups. Copyright © 2016 ISCA. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_2308457X_v08-12-September-2016_n_p3002_Ferrer |
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Universidad de Buenos Aires |
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I-28 |
| repository_str |
R-134 |
| collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
| topic |
Active learning Adaptation Annotation Speech-activity detection Artificial intelligence Budget control Speech Speech communication Speech processing Active Learning Adaptation Annotation Audio samples Baseline systems Simple approach Speech activity detections Training data Speech recognition |
| spellingShingle |
Active learning Adaptation Annotation Speech-activity detection Artificial intelligence Budget control Speech Speech communication Speech processing Active Learning Adaptation Annotation Audio samples Baseline systems Simple approach Speech activity detections Training data Speech recognition Ferrer, L. Graciarena, M. Morgan N. Georgiou P. Morgan N. Narayanan S. Metze F. Amazon Alexa; Apple; eBay; et al.; Google; Microsoft Minimizing annotation effort for adaptation of speech-activity detection systems |
| topic_facet |
Active learning Adaptation Annotation Speech-activity detection Artificial intelligence Budget control Speech Speech communication Speech processing Active Learning Adaptation Annotation Audio samples Baseline systems Simple approach Speech activity detections Training data Speech recognition |
| description |
Annotating audio data for the presence and location of speech is a time-consuming and therefore costly task. This is mostly because annotation precision greatly affects the performance of the speech-activity detection (SAD) systems trained with this data, which means that the annotation process must be careful and detailed. Although significant amounts of data are already annotated for speech presence and are available to train SAD systems, these systems are known to perform poorly on channels that are not well-represented by the training data. However obtaining representative audio samples from a new channel is relative easy and this data can be used for training a new SAD system or adapting one trained with larger amounts of mismatched data. This paper focuses on the problem of selecting the best-possible subset of available audio data given a budgeted time for annotation. We propose simple approaches for selection that lead to significant gains over na?ive methods that merely select N full files at random. An approach that uses the framelevel scores from a baseline system to select regions such that the score distribution is uniformly sampled gives the best tradeoff across a variety of channel groups. Copyright © 2016 ISCA. |
| format |
CONF |
| author |
Ferrer, L. Graciarena, M. Morgan N. Georgiou P. Morgan N. Narayanan S. Metze F. Amazon Alexa; Apple; eBay; et al.; Google; Microsoft |
| author_facet |
Ferrer, L. Graciarena, M. Morgan N. Georgiou P. Morgan N. Narayanan S. Metze F. Amazon Alexa; Apple; eBay; et al.; Google; Microsoft |
| author_sort |
Ferrer, L. |
| title |
Minimizing annotation effort for adaptation of speech-activity detection systems |
| title_short |
Minimizing annotation effort for adaptation of speech-activity detection systems |
| title_full |
Minimizing annotation effort for adaptation of speech-activity detection systems |
| title_fullStr |
Minimizing annotation effort for adaptation of speech-activity detection systems |
| title_full_unstemmed |
Minimizing annotation effort for adaptation of speech-activity detection systems |
| title_sort |
minimizing annotation effort for adaptation of speech-activity detection systems |
| url |
http://hdl.handle.net/20.500.12110/paper_2308457X_v08-12-September-2016_n_p3002_Ferrer |
| work_keys_str_mv |
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