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Learning and controlling the source-filter representation of speech with a variational autoencoder
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In: https://hal.archives-ouvertes.fr/hal-03650569 ; 2022 (2022)
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Learning and controlling the source-filter representation of speech with a variational autoencoder ...
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Repeat after me: Self-supervised learning of acoustic-to-articulatory mapping by vocal imitation ...
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High-resolution speaker counting in reverberant rooms using CRNN with Ambisonics features
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In: EUSIPCO 2020 - 28th European Signal Processing Conference (EUSIPCO) ; https://hal.archives-ouvertes.fr/hal-03537323 ; EUSIPCO 2020 - 28th European Signal Processing Conference (EUSIPCO), Jan 2021, Amsterdam, Netherlands. pp.71-75, ⟨10.23919/Eusipco47968.2020.9287637⟩ (2021)
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Alternate Endings: Improving Prosody for Incremental Neural TTS with Predicted Future Text Input
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In: Interspeech 2021 - 22nd Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03372802 ; Interspeech 2021 - 22nd Annual Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic. pp.3865-3869, ⟨10.21437/Interspeech.2021-275⟩ (2021)
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Learning robust speech representation with an articulatory-regularized variational autoencoder
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In: Proccedings of Interspeech 2021 ; Interspeech 2021 - 22nd Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-03373252 ; Interspeech 2021 - 22nd Annual Conference of the International Speech Communication Association, Aug 2021, Brno, Czech Republic (2021)
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Learning robust speech representation with an articulatory-regularized variational autoencoder ...
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Towards an articulatory-driven neural vocoder for speech synthesis
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In: ISSP 2020 - 12th International Seminar on Speech Production ; https://hal.archives-ouvertes.fr/hal-03184762 ; ISSP 2020 - 12th International Seminar on Speech Production, Dec 2020, Providence (virtual), United States (2020)
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Evaluating the Potential Gain of Auditory and Audiovisual Speech-Predictive Coding Using Deep Learning
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In: ISSN: 0899-7667 ; EISSN: 1530-888X ; Neural Computation ; https://hal.archives-ouvertes.fr/hal-03016083 ; Neural Computation, Massachusetts Institute of Technology Press (MIT Press), 2020, 32 (3), pp.596-625. ⟨10.1162/neco_a_01264⟩ (2020)
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Deeppredspeech: Computational Models Of Predictive Speech Coding Based On Deep Learning ...
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DeepPredSpeech: computational models of predictive speech coding based on deep learning ...
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DeepPredSpeech: computational models of predictive speech coding based on deep learning ...
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Extending the Cascaded Gaussian Mixture Regression Framework for Cross-Speaker Acoustic-Articulatory Mapping
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In: ISSN: 2329-9290 ; EISSN: 2329-9304 ; IEEE/ACM Transactions on Audio, Speech and Language Processing ; https://hal.archives-ouvertes.fr/hal-01485540 ; IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2017, 25 (3), pp.662-673. ⟨10.1109/TASLP.2017.2651398⟩ (2017)
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Automatic animation of an articulatory tongue model from ultrasound images of the vocal tract
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In: ISSN: 0167-6393 ; EISSN: 1872-7182 ; Speech Communication ; https://hal.archives-ouvertes.fr/hal-01578315 ; Speech Communication, Elsevier : North-Holland, 2017, 93, pp.63 - 75. ⟨10.1016/j.specom.2017.08.002⟩ (2017)
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Voice Activity Detection Based on Statistical Likelihood Ratio With Adaptive Thresholding
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In: IWAENC 2016 - International Workshop on Acoustic Signal Enhancement (IWAENC) ; https://hal.inria.fr/hal-01349776 ; IWAENC 2016 - International Workshop on Acoustic Signal Enhancement (IWAENC), Sep 2016, Xi'an, China. pp.1-5, ⟨10.1109/IWAENC.2016.7602911⟩ (2016)
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Real-Time Control of an Articulatory-Based Speech Synthesizer for Brain Computer Interfaces
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In: ISSN: 1553-734X ; EISSN: 1553-7358 ; PLoS Computational Biology ; https://hal.archives-ouvertes.fr/hal-01459706 ; PLoS Computational Biology, Public Library of Science, 2016, 12 (11), pp.e1005119. ⟨10.1371/journal.pcbi.1005119⟩ (2016)
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Abstract:
International audience ; Restoring natural speech in paralyzed and aphasic people could be achieved using a Brain-Computer Interface (BCI) controlling a speech synthesizer in real-time. To reach this goal, a prerequisite is to develop a speech synthesizer producing intelligible speech in real-time with a reasonable number of control parameters. We present here an articulatory-based speech synthesizer that can be controlled in real-time for future BCI applications. This synthesizer converts movements of the main speech articulators (tongue, jaw, velum, and lips) into intelligible speech. The articulatory-to-acoustic mapping is performed using a deep neural network (DNN) trained on electromagnetic articulography (EMA) data recorded on a reference speaker synchronously with the produced speech signal. This DNN is then used in both offline and online modes to map the position of sensors glued on different speech articulators into acoustic parameters that are further converted into an audio signal using a vocoder. In offline mode, highly intelligible speech could be obtained as assessed by perceptual evaluation performed by 12 listeners. Then, to anticipate future BCI applications, we further assessed the real-time control of the synthesizer by both the reference speaker and new speakers, in a closed-loop paradigm using EMA data recorded in real time. A short calibration period was used to compensate for differences in sensor positions and articulatory differences between new speakers and the reference speaker. We found that real-time synthesis of vowels and consonants was possible with good intelligibility. In conclusion, these results open to future speech BCI applications using such articulatory-based speech synthesizer.
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Keyword:
[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing
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URL: https://hal.archives-ouvertes.fr/hal-01459706 https://doi.org/10.1371/journal.pcbi.1005119
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Real-Time Control of an Articulatory-Based Speech Synthesizer for Brain Computer Interfaces
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