Isolated-utterance speech recognition using hidden Markov models with bounded state durations

• Published in: IEEE transactions on signal processing 1991-08, Vol.39 (8), p.1743-1752
• Main Authors: Gu, Hung-Yan, Tseng, Chiu-Yu, Lee, Lin-Shan
• Format: Article
• Language: English
• Subjects: Computer science; Hidden Markov models; History; Parametric statistics; Phase estimation; Probability density function; Speech recognition; State estimation; Vector quantization; Vocabulary
• ISSN: 1053-587X; 1941-0476
• DOI: 10.1109/78.91145

Hidden Markov models (HMMs) with bounded state durations (HMM/BSD) are proposed to explicitly model the state durations of HMMs and more accurately consider the temporal structures existing in speech signals in a simple, direct, but effective way. A series of experiments have been conducted for speaker dependent applications using 408 highly confusing first-tone Mandarin syllables as the example vocabulary. It was found that in the discrete case the recognition rate of HMM/BSD (78.5%) is 9.0%, 6.3%, and 1.9% higher than the conventional HMMs and HMMs with Poisson and gamma distribution state durations, respectively. In the continuous case (partitioned Gaussian mixture modeling), the recognition rates of HMM/BSD (88.3% with 1 mixture, 88.8% with 3 mixtures, and 89.4% with 5 mixtures) are 6.3%, 5.0%, and 5.5% higher than those of the conventional HMMs, and 5.9% (with 1 mixture), 3.9% (with 3 mixtures) and 3.1% (with 1 mixture), 1.8% (with 3 mixtures) higher than HMMs with Poisson and gamma distributed state durations, respectively.< >