This paper presented a speaker adaptable very low bit rate speech coder based on HMM (Hidden Markov Model) which includes the dynamic features, i.e., delta and delta delta parameters of speech. The performance of this...This paper presented a speaker adaptable very low bit rate speech coder based on HMM (Hidden Markov Model) which includes the dynamic features, i.e., delta and delta delta parameters of speech. The performance of this speech coder has been improved by using the dynamic features generated by an algorithm for speech parameter generation from HMM because the generated speech parameter vectors reflect not only the means of static and dynamic feature vectors but also the covariance of those. The encoder part is equivalent to an HMM based phoneme recognizer and transmits phoneme indexes, state durations, pitch information and speaker characteristics adaptation vectors to the decoder. The decoder receives those messages and concatenates phoneme HMM sequence according to the phoneme indexes. Then the decoder generates a sequence of mel cepstral coefficient vectors using HMM based speech parameter generation technique. Finally the decoder synthesizes speech by directly exciting the MLSA(Mel Log Spectrum Approximation) filter with the generated mel cepstral coefficient vectors, according to the pitch information.展开更多
文摘This paper presented a speaker adaptable very low bit rate speech coder based on HMM (Hidden Markov Model) which includes the dynamic features, i.e., delta and delta delta parameters of speech. The performance of this speech coder has been improved by using the dynamic features generated by an algorithm for speech parameter generation from HMM because the generated speech parameter vectors reflect not only the means of static and dynamic feature vectors but also the covariance of those. The encoder part is equivalent to an HMM based phoneme recognizer and transmits phoneme indexes, state durations, pitch information and speaker characteristics adaptation vectors to the decoder. The decoder receives those messages and concatenates phoneme HMM sequence according to the phoneme indexes. Then the decoder generates a sequence of mel cepstral coefficient vectors using HMM based speech parameter generation technique. Finally the decoder synthesizes speech by directly exciting the MLSA(Mel Log Spectrum Approximation) filter with the generated mel cepstral coefficient vectors, according to the pitch information.
