On the Application of Vector Quantization and Hidden Markov Models to Speaker-Independent, Isolated Word Recognition

Speaker-independent isolated word recognition using multiple hidden Markov models

“…In our experiments, a constrained left-to-right HMM structure with five states, as described by Rabiner [3], was used. The model is shown in Fig.…”

“…In the LBG case, larger codebooks require greater amount of computation, but yield comparatively small gains in recognition accuracy. Rabiner [3] suggests a codebook size of 64 for the isolated digits recognition task. In our experience a codebook with 128 members has been found to represent a reasonable balance between the amount of computation required and the resulting recognition accuracy.…”

“…It was first used for speech and image coding. Rabiner successfully constructed a VQ/HMM system which used VQ combined with HMM for speech recognition in 1983 [3]. In a VQ/HMM system, the vector quantisation works as a signal space classifier and a data compressor.…”

Gaussian Mixture and Markov Models for Cell-Phase Classification in Microscopic Imaging

“…u nt log u nt (13) where d nt is redefined as in (8), and m E > 0 is a value which controls the degree of fuzzy entropy. Minimizing the H-function with respect to λ results in the following FE-GMM algorithm 1) Given a training data set X = {x 1 , x 2 , .…”

“…It was shown in the literature that no significant difference in pattern recognition was found by using different initialisation methods [13], [14]. Therefore the following parameters of the GMM are initialised: Mixture weights, mean vectors, covariance matrices and fuzzy membership functions.…”