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Audio-Visual Speech Recognition Using Bimodal-Trained Bottleneck Features for a Person with Severe Hearing Loss.

, , , , , , and . INTERSPEECH, page 277-281. ISCA, (2016)

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Individuality-preserving voice conversion for articulation disorders based on non-negative matrix factorization., , , and . ICASSP, page 8037-8040. IEEE, (2013)Multiple Non-Negative Matrix Factorization for Many-to-Many Voice Conversion., , and . IEEE ACM Trans. Audio Speech Lang. Process., 24 (7): 1175-1184 (2016)Small-parallel exemplar-based voice conversion in noisy environments using affine non-negative matrix factorization., , , , and . EURASIP J. Audio Speech Music. Process., (2015)Speaker-Targeted Audio-Visual Speech Recognition Using a Hybrid CTC/Attention Model with Interference Loss., , , , and . ICASSP, page 251-255. IEEE, (2022)Individuality-Preserving Spectrum Modification for Articulation Disorders Using Phone Selective Synthesis., , , and . SLPAT@Interspeech, page 118-123. Association for Computational Linguistics, (2015)Visual-to-speech conversion based on maximum likelihood estimation., , , and . MVA, page 518-521. IEEE, (2017)Multimodal exemplar-based voice conversion using lip features in noisy environments., , , and . INTERSPEECH, page 1159-1163. ISCA, (2014)Exemplar-based individuality-preserving voice conversion for articulation disorders in noisy environments., , , and . INTERSPEECH, page 3637-3641. ISCA, (2013)Exemplar-based emotional voice conversion using non-negative matrix factorization., , , and . APSIPA, page 1-7. IEEE, (2014)Multimodal voice conversion using non-negative matrix factorization in noisy environments., , , and . ICASSP, page 1542-1546. IEEE, (2014)