Phoneme recognition with statistical modeling of the prediction error of neural networks

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To access the full text documents, please follow this link: http://hdl.handle.net/2117/117387

dc.contributor	Universitat Politècnica de Catalunya. Departament d'Arquitectura de Computadors
dc.contributor	Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions
dc.contributor	Universitat Politècnica de Catalunya. CNDS - Xarxes de Computadors i Sistemes Distribuïts
dc.contributor	Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla
dc.contributor.author	Freitag, Fèlix
dc.contributor.author	Monte Moreno, Enrique
dc.date	1998
dc.identifier.citation	Freitag, F., Monte, E. Phoneme recognition with statistical modeling of the prediction error of neural networks. A: International Conference on Spoken Language Processing. "ICSLP 98: the 5th International Conference on Spoken Language Processing; incorporating the 7th Australian International Speech Science and Technology Conference; Sydney Convention Centre, Sydney, Australia, 30th November-4th December 1998". Baixas: International Speech Communication Association (ISCA), 1998.
dc.identifier.citation	1-876346-17- 5
dc.identifier.citation	http://mirlab.org/conference_papers/International_Conference/ICSLP%201998/PDF/SCAN/SL980455.PDF
dc.identifier.uri	http://hdl.handle.net/2117/117387
dc.language.iso	eng
dc.publisher	International Speech Communication Association (ISCA)
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic
dc.subject	Automatic speech recognition
dc.subject	Reconeixement automàtic de la parla
dc.title	Phoneme recognition with statistical modeling of the prediction error of neural networks
dc.type	info:eu-repo/semantics/publishedVersion
dc.type	info:eu-repo/semantics/conferenceObject
dc.description.abstract	This paper presents a speech recognition system which incorporates predictive neural networks. The neural networks are used to predict observation vectors of speech. The prediction error vectors are modeled on the state level by Gaussian densities, which provide the local similarity measure for the Viterbi algorithm during recognition. The system is evaluated on a continuous speech phoneme recognition task. Compared with a HMM reference system, the proposed system obtained better results in the speech recognition experiments.
dc.description.abstract	Peer Reviewed

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