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"This is Houston. Say again, please". The Behavox system for the Apollo-11 Fearless Steps Challenge (phase II) ...
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Abstract:
We describe the speech activity detection (SAD), speaker diarization (SD), and automatic speech recognition (ASR) experiments conducted by the Behavox team for the Interspeech 2020 Fearless Steps Challenge (FSC-2). A relatively small amount of labeled data, a large variety of speakers and channel distortions, specific lexicon and speaking style resulted in high error rates on the systems which involved this data. In addition to approximately 36 hours of annotated NASA mission recordings, the organizers provided a much larger but unlabeled 19k hour Apollo-11 corpus that we also explore for semi-supervised training of ASR acoustic and language models, observing more than 17% relative word error rate improvement compared to training on the FSC-2 data only. We also compare several SAD and SD systems to approach the most difficult tracks of the challenge (track 1 for diarization and ASR), where long 30-minute audio recordings are provided for evaluation without segmentation or speaker information. For all ... : Accepted to Interspeech 2020 ...
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Keyword:
Audio and Speech Processing eess.AS; Computation and Language cs.CL; FOS Computer and information sciences; FOS Electrical engineering, electronic engineering, information engineering; Sound cs.SD
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URL: https://arxiv.org/abs/2008.01504
https://dx.doi.org/10.48550/arxiv.2008.01504
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Conversational telephone speech recognition for Lithuanian
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In: ISSN: 0885-2308 ; EISSN: 1095-8363 ; Computer Speech and Language ; https://hal.archives-ouvertes.fr/hal-01837147 ; Computer Speech and Language, Elsevier, 2018, 49, pp.71-82 (2018)
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An investigation into language model data augmentation for low-resourced STT and KWS
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In: IEEE International Conference on Acoustics, Speech, and Signal Processing ; https://hal.archives-ouvertes.fr/hal-01837171 ; IEEE International Conference on Acoustics, Speech, and Signal Processing, IEEE, Mar 2017, New Orleans, United States (2017)
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Language Model Data Augmentation for Keyword Spotting
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In: Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-01837186 ; Annual Conference of the International Speech Communication Association , Jan 2016, San Francisco, United States (2016)
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| 5 |
Machine Translation Based Data Augmentation for Cantonese Keyword Spotting (Author's Manuscript)
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Explicit trajectories and speaker class modeling for child and adult speech recognition ; Modélisation de trajectoires et de classes de locuteurs pour la reconnaissance de voix d'enfants et d'adultes
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In: XXXème édition des Journées d'Etudes sur la Parole ; https://hal.inria.fr/hal-01080343 ; XXXème édition des Journées d'Etudes sur la Parole, Jun 2014, Le Mans, France (2014)
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Component Structuring and Trajectory Modeling for Speech Recognition
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In: Interspeech ; https://hal.inria.fr/hal-01063653 ; Interspeech, Sep 2014, Singapoore, Singapore (2014)
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Efficient constrained parametrization of GMM with class-based mixture weights for Automatic Speech Recognition
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In: LTC'13 - 6th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics ; https://hal.inria.fr/hal-00923202 ; LTC'13 - 6th Language & Technology Conference: Human Language Technologies as a Challenge for Computer Science and Linguistics, Dec 2013, Poznań, Poland (2013)
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