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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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Abstract:
Recent breakthroughs in automatic speech recognition (ASR) have resulted in a word error rate (WER) on par with human transcribers on the English Switchboard benchmark. However, dealing with linguistic mismatches between the training and testing data is still a significant challenge that remains unsolved. Under the monolingual environment, it is well-known that the performance of ASR systems degrades significantly when presented with the speech from speakers with different accents, dialects, and speaking styles than those encountered during system training. Under the multi-lingual environment, ASR systems trained on a source language achieve even worse performance when tested on another target language because of mismatches in terms of the number of phonemes, lexical ambiguity, and power of phonotactic constraints provided by phone-level n-grams. In order to address the issues of linguistic mismatches for current ASR systems, my dissertation investigates both knowledge-gnostic and knowledge-agnostic solutions. In the first part, classic theories relevant to acoustics and articulatory phonetics that present capability of being transferred across a dialect continuum from local dialects to another standardized language are re-visited. Experiments demonstrate the potentials that acoustic correlates in the vicinity of landmarks could help to build a bridge for dealing with mismatches across difference local or global varieties in a dialect continuum. In the second part, we design an end-to-end acoustic modeling approach based on connectionist temporal classification loss and propose to link the training of acoustics and accent altogether in a manner similar to the learning process in human speech perception. This joint model not only performed well on ASR with multiple accents but also boosted accuracies of accent identification task in comparison to separately-trained models.
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Keyword:
Acoustic Landmarks; Acoustic Modeling; Acoustic Phonetics; Automatic Speech Recognition; Connectionist Temporal Classification; Deep Learning; Distinctive Features; End-to-End; Model Compression; Multi-Accents; Multi-Lingual; Multi-Task Learning; Pronunciation Error Detection
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URL: http://hdl.handle.net/2142/105187
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Speech recognition with probabilistic transcriptions and end-to-end systems using deep learning
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| 9 |
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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| 11 |
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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| 12 |
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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| 14 |
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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| 17 |
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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| 18 |
Anger Recognition in Speech Using Acoustic and Linguistic Cues
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: Elsevier, 2013
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| 19 |
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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