Title :
A study of the HMM for speaker-independent isolated word recognition
Author :
Neelakantan, V. ; Gowdy, J.
Author_Institution :
Dept. of Electr. & Comput. Eng., Clemson Univ., SC, USA
Abstract :
Two new additions in the design of hidden Markov models (HMMs) are proposed. First, a scheme to incorporate the geometric arrangement of the codebook vectors in their vector space is designed. This information is included in the HMM parameters via smoothing after the initial training phase. Second, a scheme to remove the imbalance in the output symbol probability matrix is devised. This scheme involves the use of time-reversed discretized utterances and training the model from the last state to the first one in the training phase of the HMM design. The various schemes were tested on a 2-speaker, 20-word vocabulary task. The results are given
Keywords :
Markov processes; speech recognition; HMM parameters; codebook vectors; hidden Markov models; isolated word recognition; output symbol probability matrix; speaker independent recognition; speech recognition; time reversed utterances; training; vector space; vocabulary; Databases; Digital filters; Hidden Markov models; Iterative algorithms; Linear predictive coding; Smoothing methods; Speech recognition; Testing; Training data; Vocabulary;
Conference_Titel :
Southeastcon '90. Proceedings., IEEE
Conference_Location :
New Orleans, LA
DOI :
10.1109/SECON.1990.117777
