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1
Using Automatic Speech Recognition to Optimize Hearing-Aid Time Constants
In: ISSN: 1662-4548 ; EISSN: 1662-453X ; Frontiers in Neuroscience ; https://hal.archives-ouvertes.fr/hal-03627441 ; Frontiers in Neuroscience, Frontiers, 2022, 16 (779062), ⟨10.3389/fnins.2022.779062⟩ ; https://www.frontiersin.org/articles/10.3389/fnins.2022.779062/full (2022)
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2
RETRIEVING SPEAKER INFORMATION FROM PERSONALIZED ACOUSTIC MODELS FOR SPEECH RECOGNITION
In: IEEE ICASSP 2022 ; https://hal.archives-ouvertes.fr/hal-03539741 ; IEEE ICASSP 2022, 2022, Singapour, Singapore (2022)
Abstract: International audience ; The widespread of powerful personal devices capable of collecting voice of their users has opened the opportunity to build speaker adapted speech recognition system (ASR) or to participate to collaborative learning of ASR. In both cases, personalized acoustic models (AM), i.e. fine-tuned AM with specific speaker data, can be built. A question that naturally arises is whether the dissemination of personalized acoustic models can leak personal information. In this paper, we show that it is possible to retrieve the gender of the speaker, but also his identity, by just exploiting the weight matrix changes of a neural acoustic model locally adapted to this speaker. Incidentally we observe phenomena that may be useful towards explainability of deep neural networks in the context of speech processing. Gender can be identified almost surely using only the first layers and speaker verification performs well when using middle-up layers. Our experimental study on the TED-LIUM 3 dataset with HMM/TDNN models shows an accuracy of 95% for gender detection, and an Equal Error Rate of 9.07% for a speaker verification task by only exploiting the weights from personalized models that could be exchanged instead of user data.
Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; acoustic model; Automatic speech recognition; collaborative learning; personalized acoustic models; speaker information
URL: https://hal.archives-ouvertes.fr/hal-03539741
https://hal.archives-ouvertes.fr/hal-03539741/document
https://hal.archives-ouvertes.fr/hal-03539741/file/ICASSP_2022_SpeakerAnalysisInfoPrivacyVF.pdf
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3
Emotional Speech Recognition Using Deep Neural Networks
In: ISSN: 1424-8220 ; Sensors ; https://hal.archives-ouvertes.fr/hal-03632853 ; Sensors, MDPI, 2022, 22 (4), pp.1414. ⟨10.3390/s22041414⟩ (2022)
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4
The Impact of Removing Head Movements on Audio-visual Speech Enhancement
In: ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing ; https://hal.inria.fr/hal-03551610 ; ICASSP 2022 - IEEE International Conference on Acoustics, Speech and Signal Processing, IEEE Signal Processing Society, May 2022, Singapore, Singapore. pp.1-5 (2022)
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5
Fine-tuning pre-trained models for Automatic Speech Recognition: experiments on a fieldwork corpus of Japhug (Trans-Himalayan family)
In: https://halshs.archives-ouvertes.fr/halshs-03647315 ; 2022 (2022)
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6
Dvoice : An open source dataset for Automatic Speech Recognition on African Languages and Dialects ...
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7
Dvoice : An open source dataset for Automatic Speech Recognition on African Languages and Dialects ...
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8
ASR training dataset for Croatian ParlaSpeech-HR v1.0
Ljubešić, Nikola; Koržinek, Danijel; Rupnik, Peter. - : Jožef Stefan Institute, 2022
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9
Review on multichannel emotion perception in ASD (Zhang et al., 2022) ...
Zhang, Minyue; Chen, Yu; Lin, Yi. - : ASHA journals, 2022
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10
Review on multichannel emotion perception in ASD (Zhang et al., 2022) ...
Zhang, Minyue; Chen, Yu; Lin, Yi. - : ASHA journals, 2022
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11
Towards a Perceptual Model for Estimating the Quality of Visual Speech ...
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12
An error correction scheme for improved air-tissue boundary in real-time MRI video for speech production ...
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13
Expression-preserving face frontalization improves visually assisted speech processing ...
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14
Freeform Body Motion Generation from Speech ...
Xu, Jing; Zhang, Wei; Bai, Yalong. - : arXiv, 2022
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15
Common Phone: A Multilingual Dataset for Robust Acoustic Modelling ...
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Common Phone: A Multilingual Dataset for Robust Acoustic Modelling ...
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17
Interactions Among Reverberation, WDRC, and WM (Reinhart & Souza, 2016) ...
Reinhart, Paul N.; Souza, Pamela E.. - : ASHA journals, 2022
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18
Robust speech recognition for low-resource languages ...
Romanenko, Aleksei. - : Universität Ulm, 2022
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19
Effects of Spatial Speech Presentation on Listener Response Strategy for Talker-Identification ...
Uhrig, Stefan; Perkis, Andrew; Möller, Sebastian. - : Technische Universität Berlin, 2022
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20
Detection and Recognition of Asynchronous Auditory/Visual Speech: Effects of Age, Hearing Loss, and Talker Accent ...
Gordon-Salant, Sandra; Schwartz, Maya; Oppler, Kelsey. - : Digital Repository at the University of Maryland, 2022
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