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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 3 of 3 1 Some issues affecting the transcription of hungarian broadcast audio Roy, Anindya; Lamel, Lori; Fraga Da Silva, Thiago... In: Annual Conference of the International Speech Communication Association ; https://hal.archives-ouvertes.fr/hal-01843430 ; Annual Conference of the International Speech Communication Association , Aug 2013, Lyon, France (2013) BASE Show details 2 Structured output layer neural network language models for speech recognition Le, Hai Son; Oparin, Ilya; Allauzen, Alexandre; Gauvain, Jean-Luc; Yvon, François In: ISSN: 2329-9290 ; EISSN: 2329-9304 ; IEEE/ACM Transactions on Audio, Speech and Language Processing ; https://hal.archives-ouvertes.fr/hal-01908377 ; IEEE/ACM Transactions on Audio, Speech and Language Processing, Institute of Electrical and Electronics Engineers, 2013, 21, pp.197-206 (2013) Abstract: International audience ; This paper extends a novel neural network language model (NNLM) which relies on word clustering to structure the output vocabulary: Structured OUtput Layer (SOUL) NNLM. This model is able to handle arbitrarily-sized vocabularies, hence dispensing with the need for shortlists that are commonly used in NNLMs. Several softmax layers replace the standard output layer in this model. The output structure depends on the word clustering which is based on the continuous word representation determined by the NNLM. Mandarin and Arabic data are used to evaluate the SOUL NNLM accuracy via speech-to-text experiments. Well tuned speech-to-text systems (with error rates around 10%) serve as the baselines. The SOUL model achieves consistent improvements over a classical shortlist NNLM both in terms of perplexity and recognition accuracy for these two languages that are quite different in terms of their internal structure and recognition vocabulary size. An enhanced training scheme is proposed that allows more data to be used at each training iteration of the neural network. Keyword: [INFO.INFO-CL]Computer Science [cs]/Computation and Language [cs.CL]; [INFO]Computer Science [cs] URL: https://hal.archives-ouvertes.fr/hal-01908377 BASE Hide details 3 Acoustic unit discovery and pronunciation generation from a grapheme-based lexicon Hartmann, William; Roy, Anindya; Lamel, Lori... In: IEEE Automatic Speech Recognition and Understanding Workshop ; https://hal.archives-ouvertes.fr/hal-01843433 ; IEEE Automatic Speech Recognition and Understanding Workshop, Dec 2013, Olomouc, Czech Republic (2013) BASE Show details

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