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Learning speech embeddings for speaker adaptation and speech understanding
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Towards unsupervised learning of speech features in the wild
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In: SLT 2020 : IEEE Spoken Language Technology Workshop ; https://hal.archives-ouvertes.fr/hal-03070411 ; SLT 2020 : IEEE Spoken Language Technology Workshop, Dec 2020, Shenzhen / Virtual, China (2020)
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Achieving Multi-Accent ASR via Unsupervised Acoustic Model Adaptation
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In: INTERSPEECH 2020 ; https://hal.inria.fr/hal-02907929 ; INTERSPEECH 2020, Oct 2020, Shanghai, China (2020)
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Preschoolers' Attention to Emotional Prosody as a Function of Speaker Conventionality ...
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Learning to adapt: meta-learning approaches for speaker adaptation
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Introducing Phonetic Information to Speaker Embedding for Speaker Verification
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In: Electrical and Computer Engineering Faculty Publications (2019)
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Abstract:
Phonetic information is one of the most essential components of a speech signal, playing an important role for many speech processing tasks. However, it is difficult to integrate phonetic information into speaker verification systems since it occurs primarily at the frame level while speaker characteristics typically reside at the segment level. In deep neural network-based speaker verification, existing methods only apply phonetic information to the frame-wise trained speaker embeddings. To improve this weakness, this paper proposes phonetic adaptation and hybrid multi-task learning and further combines these into c-vector and simplified c-vector architectures. Experiments on National Institute of Standards and Technology (NIST) speaker recognition evaluation (SRE) 2010 show that the four proposed speaker embeddings achieve better performance than the baseline. The c-vector system performs the best, providing over 30% and 15% relative improvements in equal error rate (EER) for the core-extended and 10 s–10 s conditions, respectively. On the NIST SRE 2016, 2018, and VoxCeleb datasets, the proposed c-vector approach improves the performance even when there is a language mismatch within the training sets or between the training and evaluation sets. Extensive experimental results demonstrate the effectiveness and robustness of the proposed methods.
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Keyword:
C-vector; Deep neural networks; Electrical and Computer Engineering; Multi-task learning; Phonetic adaptation; Speaker embedding; Speaker verification
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URL: https://uknowledge.uky.edu/ece_facpub/37 https://uknowledge.uky.edu/cgi/viewcontent.cgi?article=1037&context=ece_facpub
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Speaker-Adapted Confidence Measures for ASR using Deep Bidirectional Recurrent Neural Networks
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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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Articulatory representations to address acoustic variability in speech ...
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Articulatory representations to address acoustic variability in speech
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Adaptation au locuteur pour la séparation de la parole par NMF
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In: https://hal.sorbonne-universite.fr/hal-01482183 ; [Stage] STMS - Sciences et Technologies de la Musique et du Son UMR 9912 IRCAM-CNRS-UPMC. 2016 (2016)
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Iterative PLDA Adaptation for Speaker Diarization
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In: Interspeech 2016 ; https://hal.archives-ouvertes.fr/hal-01433172 ; Interspeech 2016, Sep 2016, San Francisco, United States. pp.2175 - 2179, ⟨10.21437/Interspeech.2016-572⟩ (2016)
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Speaker-dependent Multipitch Tracking Using Deep Neural Networks
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Phonetic reduction in spontaneous speech by children aged 9-14 years
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In: Presented at: 18th International Congress of Phonetic Sciences, Glasgow, UK. (2015) (2015)
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The Acquisition of Vowel Normalization during Early Infancy: Theory and Computational Framework
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In: http://rave.ohiolink.edu/etdc/view?acc_num=osu1388689249 (2014)
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'All the better for not seeing you': effects of communicative context on the speech of an individual with acquired communication difficulties.
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In: J Commun Disord , 46 (5-6) 475 - 483. (2013) (2013)
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Contributions to Adaptation on Automatic Speech Recognition and Multilingual Handwritten Text Recognition
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