Speech modelling using cepstral-time feature matrices in hidden Markov models
作者:
S.V.Vaseghi,
P.N.Conner,
B.P.Milner,
期刊:
IEE Proceedings I (Communications, Speech and Vision)
(IET Available online 1993)
卷期:
Volume 140,
issue 5
页码: 317-320
年代: 1993
DOI:10.1049/ip-i-2.1993.0046
出版商: IEE
数据来源: IET
摘要:
The paper explores the use of 2-dimensional cepstral-time features for the utilisation of correlation among successive speech spectral vectors, within a hidden-Markov-model (HMM) framework. A cepstral-time-feature matrix is obtained from a 2-dimensional discrete cosine transform of a spectral-time matrix. Advantages of cepstral-time features are that cepstral-time-feature matrices are a simple and robust method of representing short-time variation of speech spectral parameters; a cepstral-time matrix contains information on the transitional dynamics of feature vectors within the matrix; speech recognition based on cepstral time matrices is more robust in noisy environments; and use of a matrix ofMcepstral vectors implies a minimum HMM-state duration constraint ofMvector units. A simple framework investigated in the paper for applications of cepstral-time features is a finite-state-matrix quantiser (FSMQ), a special case of the HMM. It is used for initialisation of the training phase of HMMs.
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