Title :
On increasing structural complexity of finite state speech models
Author :
Vaseghi, S.V. ; Conner, P.
Author_Institution :
Sch. of Inf. Syst., East Anglia Univ., Norwich, UK
Abstract :
Some methods of modeling speech spectral features and duration within the framework of finite-state models are discussed. On observation modeling, the use of cepstral-time matrices, instead of cepstral vectors, as the observation unit is investigated. On duration modeling, a new HMM is introduced in which state transition and duration probabilities are combined to form duration-dependent transition probabilities. The duration dependent transitions are derived from the cumulative density function (CDF) of state duration
Keywords :
hidden Markov models; probability; speech analysis and processing; speech recognition; HMM; cepstral vectors; cepstral-time matrices; cumulative density function; duration modeling; duration-dependent transition probabilities; finite state speech models; observation modeling; observation unit; spectral features; speech duration; speech recognition; state duration probability; state transition probability; structural complexity; Automatic speech recognition; Bit rate; Cepstral analysis; Density functional theory; Distribution functions; Hidden Markov models; Information systems; Probability; Quantization; Speech recognition;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0532-9
DOI :
10.1109/ICASSP.1992.225852
