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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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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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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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Abstract:
International audience ; Acoustic modeling techniques, based on clustering of the training data, have become essential in large vocabulary continuous speech recognition (LVCSR) systems. Clustered data (supervised or unsupervised) is typically used to estimate the sets of parameters by adapting the speaker-independent model on each subset. For Hidden Markov Models with Gaussian mixture observation densities (HMM-GMM) most of the adaptation techniques are focusing on re-estimation of the mean vectors, whereas the mixture weights are typically distributed almost uniformly. In this work we propose a way of specifying the subspaces of the GMM by associating the sets of Gaussian mixture weights with the speaker classes and sharing the Gaussian parameters across speaker classes. The method allows us to better parametrize GMM without increasing significantly the number of model parameters. Our experiments on French radio broadcast data demonstrate the improvement of the accuracy with such parametrization compared to the models with similar, or even larger number of parameters.
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Keyword:
[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing; [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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URL: https://hal.inria.fr/hal-00923202
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