Title :
Large-vocabulary speaker-independent continuous speech recognition using HMM
Author :
Lee, Kai-Fu ; Hon, Hsiao-Wuen
Author_Institution :
Dept. of Comput. Sci., Carnegie-Mellon Univ., Pittsburgh, PA, USA
Abstract :
SPHINX, the first large-vocabulary speaker-independent continuous-speech recognizer is described. SPHINX is a hidden-Markov-model (HMM)-based recognizer using multiple codebooks of various LPC-derived features. Two types of HMMs are used in SPHINX: context-independent phone models and function-word-dependent phone models. On a 997-word task using a bigram grammar, SPHINX achieved a word accuracy of 93%. This demonstrates the feasibility of speaker-independent continuous-speech recognition, and the appropriateness of hidden Markov models for such a task
Keywords :
Markov processes; encoding; speech recognition; LPC-derived features; SPHINX; bigram grammar; context-independent phone models; function-word-dependent phone models; hidden-Markov-model; large vocabulary speech recognition; multiple codebooks; speaker-independent continuous speech recognition; word accuracy; Autocorrelation; Cepstral analysis; Cepstrum; Computer science; Context modeling; Hidden Markov models; Linear predictive coding; Speech processing; Speech recognition; System testing;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on
Conference_Location :
New York, NY
DOI :
10.1109/ICASSP.1988.196527
