期刊
IEEE SIGNAL PROCESSING LETTERS
卷 12, 期 2, 页码 166-169出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/LSP.2004.840914
关键词
speech recognition; switching autoregressive processes
Linear predictive hidden Markov modeling is compared with a simple form of the switching autoregressive process. The latter process captures existing signal correlation during transitions of the Markov chain. Parameter estimation is described using naturally stable forward-backward recursions. The switching autoregressive model outperformed the linear predictive model in a digit recognition task and provided comparable performance to a cepstral-based recognizer.
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