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Master's Dissertation
DOI
10.11606/D.3.2001.tde-05052003-104044
Document
Author
Full name
Liselene de Abreu Borges
E-mail
Institute/School/College
Knowledge Area
Date of Defense
Published
São Paulo, 2001
Supervisor
Committee
Ramirez, Miguel Arjona (President)
Fagundes, Rubem Dutra Ribeiro
Sanches, Ivandro
Title in Portuguese
Sistemas de adaptação ao locutor utilizando autovozes.
Keywords in Portuguese
adaptação ao locutor
autovozes
reconhecimento de voz
Abstract in Portuguese
O presente trabalho descreve duas técnicas de adaptação ao locutor para sistemas de reconhecimento de voz utilizando um volume de dados de adaptação reduzido. Regressão Linear de Máxima Verossimilhança (MLLR) e Autovozes são as técnicas trabalhadas. Ambas atualizam as médias das Gaussianas dos modelos ocultos de Markov (HMM). A técnica MLLR estima um grupo de transformações lineares para os parâmetros das medias das Gaussianas do sistema. A técnica de Autovozes baseia-se no conhecimento prévio das variações entre locutores. Para obtermos o conhecimento prévio, que está contido nas autovozes, utiliza-se a análise em componentes principais (PCA). Fizemos os testes de adaptação das médias em um sistema de reconhecimento de voz de palavras isoladas e de vocabulário restrito. Contando com um volume grande de dados de adaptação (mais de 70% das palavras do vocabulário) a técnica de autovozes não apresentou resultados expressivos com relação aos que a técnica MLLR apresentou. Agora, quando o volume de dados reduzido (menos de 15% das palavras do vocabulário) a técnica de Autovozes apresentou-se superior à MLLR.
Title in English
Speaker adaptation system using eigenvoices.
Keywords in English
eigenvoices
speaker adaptation
speech recognition
Abstract in English
This present work describe two speaker adaptation technique, using a small amount of adaptation data, for a speech recognition system. These techniques are Maximum Likelihood Linear Regression (MLLR) and Eigenvoices. Both re-estimates the mean of a continuous density Hidden Markov Model system. MLLR technique estimates a set of linear transformations for mean parameters of a Gaussian system. The eigenvoice technique is based on a previous knowledge about speaker variation. For obtaining this previous knowledge, that are retained in eigenvoices, it necessary to apply principal component analysis (PCA). We make adaptation tests over an isolated word recognition system, restrict vocabulary. If a large amount of adaptation data is available (up to 70% of all vocabulary) Eigenvoices technique does not appear to be a good implementation if compared with the MLLR technique. Now, when just a small amount of adaptation data is available (less than 15 % of all vocabulary), Eigenvoices technique get better results than MLLR technique.
 
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Dmestrado.pdf (569.51 Kbytes)
Publishing Date
2003-05-09
 
WARNING: The material described below relates to works resulting from this thesis or dissertation. The contents of these works are the author's responsibility.
  • BORGES, L. A., ARJONA RAMÍREZ, M., and FAGUNDES, R. D. R. Speaker Adaptation Using Eigenvoices Technique. In ITS2002 SBrT/IEEE International Telecommunications Symposium, Natal, 2002. Proceedings of ITS2002 SBrT/IEEE International Telecommunications Symposium., 2002. Available from: http://www.lps.usp.br/%7Emiguel/publica/ITS2002.zip.
All rights of the thesis/dissertation are from the authors
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