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Influence of Highly Inflected Word Forms and Acoustic Background on the Robustness of Automatic Speech Recognition for Human–Computer Interaction
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In: Mathematics; Volume 10; Issue 5; Pages: 711 (2022)
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Discriminative feature modeling for statistical speech recognition ...
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Cross-lingual acoustic modeling in upper sorbian - preliminary study
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In: Fraunhofer IKTS (2021)
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Glottal Stops in Upper Sorbian: A Data-Driven Approach
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In: Fraunhofer IKTS (2021)
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Estimating the Degree of Sleepiness by Integrating Articulatory Feature Knowledge in Raw Waveform Based CNNS ...
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Estimating the Degree of Sleepiness by Integrating Articulatory Feature Knowledge in Raw Waveform Based CNNS ...
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Dealing with linguistic mismatches for automatic speech recognition
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Speech recognition with probabilistic transcriptions and end-to-end systems using deep learning
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Phonetic Context Embeddings for DNN-HMM Phone Recognition
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In: Interspeech 2016 ; https://hal.sorbonne-universite.fr/hal-02166078 ; Interspeech 2016, Sep 2016, SAN FRANCISCO, United States. pp.405-409, ⟨10.21437/Interspeech.2016-1036⟩ (2016)
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Abstract:
International audience ; This paper proposes an approach, named phonetic context embedding , to model phonetic context effects for deep neural network hidden Markov model (DNN-HMM) phone recognition. Phonetic context embeddings can be regarded as continuous and distributed vector representations of context-dependent phonetic units (e.g., triphones). In this work they are computed using neural networks. First, all phone labels are mapped into vectors of binary distinctive features (DFs, e.g., nasal/not-nasal). Then for each speech frame the corresponding DF vector is concatenated with DF vectors of previous and next frames and fed into a neural network that is trained to estimate the acoustic coefficients (e.g., MFCCs) of that frame. The values of the first hidden layer represent the embedding of the input DF vectors. Finally, the resulting embeddings are used as secondary task targets in a multi-task learning (MTL) setting when training the DNN that computes phone state posteriors. The approach allows to easily encode a much larger context than alternative MTL-based approaches. Results on TIMIT with a fully connected DNN shows phone error rate (PER) reductions from 22.4% to 21.0% and from 21.3% to 19.8% on the test core and the validation set respectively and lower PER than an alternative strong MTL approach.
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Keyword:
[SCCO.LING]Cognitive science/Linguistics; [SCCO]Cognitive science; acoustic modeling; Index Terms: embeddings; multi-task learning; speech recog- nition
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URL: https://hal.sorbonne-universite.fr/hal-02166078 https://doi.org/10.21437/Interspeech.2016-1036
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Modeling of a rise-fall intonation pattern in the language of young Paris Speakers
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In: Speech Prosody ; https://halshs.archives-ouvertes.fr/halshs-01069584 ; Speech Prosody, 2014, 7, pp.814-818 (2014)
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Vers une modélisation acoustique de l'intonation des jeunes en région parisienne : une question de " proximité " ?
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In: ISSN: 1661-8246 ; EISSN: 1661-8246 ; Nouveaux Cahiers de Linguistique Française ; https://halshs.archives-ouvertes.fr/halshs-01069593 ; Nouveaux Cahiers de Linguistique Française, Université de Genève, 2014, 31, pp.257-171 (2014)
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Towards the automatic processing of Yongning Na (Sino-Tibetan): developing a 'light' acoustic model of the target language and testing 'heavyweight' models from five national languages
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In: Proceedings of the 4th International Workshop on Spoken Language Technologies for Under-resourced Languages (SLTU 2014) ; 4th International Workshop on Spoken Language Technologies for Under-resourced Languages (SLTU 2014) ; https://halshs.archives-ouvertes.fr/halshs-00980431 ; 4th International Workshop on Spoken Language Technologies for Under-resourced Languages (SLTU 2014), May 2014, St Petersburg, Russia. pp.153-160 (2014)
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Modélisation acoustico-phonétique de langues peu dotées : Études phonétiques et travaux de reconnaissance automatique en luxembourgois
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In: Journées d'Etude sur la Parole ; https://hal.archives-ouvertes.fr/hal-01843399 ; Journées d'Etude sur la Parole, Jan 2014, Le Mans, France (2014)
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Speech Alignment and Recognition Experiments for Luxembourgish
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In: Proceedings of the 4th International Workshop on Spoken Language Technologies for Underresourced Languages ; 4th International Workshop on Spoken Language Technologies for Underresourced Languages ; https://hal.archives-ouvertes.fr/hal-01134824 ; 4th International Workshop on Spoken Language Technologies for Underresourced Languages, May 2014, Saint-Petersbourg, Russia. pp.53-60 ; http://www.mica.edu.vn/sltu2014/ (2014)
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A First LVCSR System for Luxembourgish, a Low-Resourced European Language
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In: Human Language Technology Challenges for Computer Science and Linguistics ; https://hal.archives-ouvertes.fr/hal-01135103 ; Zygmunt Vetulani; Joseph Mariani. Human Language Technology Challenges for Computer Science and Linguistics, 8387, Springer International Publishing, pp.479-490, 2014, 5th Language and Technology Conference, LTC 2011, Poznań, Poland, November 25--27, 2011, Revised Selected Papers, 978-3-319-08957-7. ⟨10.1007/978-3-319-08958-4_39⟩ (2014)
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Impact of Video Modeling Techniques on Efficiency and Effectiveness of Clinical Voice Assessment
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In: http://rave.ohiolink.edu/etdc/view?acc_num=miami1398686540 (2014)
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Anger Recognition in Speech Using Acoustic and Linguistic Cues
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: Elsevier, 2013
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Detection of acoustic-phonetic landmarks in mismatched conditions using a biomimetic model of human auditory processing
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In: http://www.isle.uiuc.edu/%7Esborys/king_coling12.pdf (2012)
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Detection of acoustic-phonetic landmarks in mismatched conditions using a biomimetic model of human auditory processing
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In: http://aclweb.org/anthology/C/C12/C12-2058.pdf (2012)
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