Combined quantized and continuous feature vector HMM approach to speech recognition
OwnerEiichi Tsubokainventor
Life of this patent
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Abstract
A device capable of achieving recognition at a high accuracy and with fewer calculations and which utilizes an HMM. The present device has a vector quantizing circuit generating a model by quantizing vectors of a training pattern having a vector series, and converting the vectors into a label series of clusters to which they belong, a continuous distribution probability density HMM generating circuit for generating a continuous distribution probability density HMM from a quantized vector series corresponding to each label of the label series, and a label incidence calculating circuit for calculating the incidence of the labels in each state from the training vectors classified in the same clusters and the continuous distribution probability density HMM.
Claims13
An HMM generator, comprising:
- vector quantizing means for generating a model by quantizing vectors of a training pattern having a vector series, and converting said quantizing vectors into a label series of clusters to which they belong,
- continuous probability distribution density HMM generating means for generating a continuous probability distribution density HMM from a quantized vector series corresponding to each label of said label series of clusters, and
- label incidence calculating means for calculating the incidence of the labels in each state from said quantizing vectors of a training pattern classified in the same label series of clusters and the continuous probability distribution density HMM.
An HMM generator, comprising:
- word pattern memory means for storing a training pattern and generating a series of feature vectors;
- vector quantizing means connected to said word pattern memory means for quantizing vectors of a training pattern received from said word pattern memory means and converting said quantizing vectors into a label series of clusters to which they belong;
- buffer memory means connected to said vector quantizing means for temporarily storing training word patterns of a word converted at said vector quantizing means;
- parameter estimating means connected to said buffer memory means for generating a model corresponding to said word converted at said vector quantizing means;
- parameter memory means connected to said parameter estimating means for storing re-estimated values of at least a transition probability for various states;
- and
- label incidence calculating means connected to said parameter memory means for calculating the incidence of the labels in each state from said quantizing vectors of a training pattern classified in the same label series of clusters and the continuous probability distribution density HMM.
Dependencies are inferred from the claim text.