Pattern recognition scheme using probabilistic models based on mixtures distribution of discrete distribution
First Claim
1. A pattern recognition method, comprising the steps of:
- calculating a probability of each probabilistic model expressing features of each recognition category with respect to each input feature vector derived from each input signal, wherein the probabilistic model represents a feature parameter subspace in which feature vectors of each recognition category exist and the feature parameter subspace is expressed by using mixture distributions of one-dimensional discrete distributions with arbitrary distribution shapes which are arranged in respective dimensions; and
outputting a recognition category expressed by a probabilistic model with a highest probability among a plurality of probabilistic models as a recognition result.
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Abstract
A pattern recognition scheme using probabilistic models that are capable of reducing a calculation cost for the output probability while improving a recognition performance even when a number of mixture component distributions of respective states is small, by arranging distributions with low calculation cost and high expressive power as the mixture component distribution. In this pattern recognition scheme, a probability of each probabilistic model expressing features of each recognition category with respect to each input feature vector derived from each input signal is calculated, where the probabilistic model represents a feature parameter subspace in which feature vectors of each recognition category exist and the feature parameter subspace is expressed by using mixture distributions of one-dimensional discrete distributions with arbitrary distribution shapes which are arranged in respective dimensions. Then, a recognition category expressed by a probabilistic model with a highest probability among a plurality of probabilistic models is outputted as a recognition result.
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Citations
28 Claims
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1. A pattern recognition method, comprising the steps of:
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calculating a probability of each probabilistic model expressing features of each recognition category with respect to each input feature vector derived from each input signal, wherein the probabilistic model represents a feature parameter subspace in which feature vectors of each recognition category exist and the feature parameter subspace is expressed by using mixture distributions of one-dimensional discrete distributions with arbitrary distribution shapes which are arranged in respective dimensions; and outputting a recognition category expressed by a probabilistic model with a highest probability among a plurality of probabilistic models as a recognition result. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8, 9, 10)
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11. A pattern recognition apparatus, comprising:
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a calculation unit for calculating a probability of each probabilistic model expressing features of each recognition category with respect to each input feature vector derived from each input signal, wherein the probabilistic model represents a feature parameter subspace in which feature vectors of each recognition category exist and the feature parameter subspace is expressed by using mixture distributions of one-dimensional discrete distributions with arbitrary distribution shapes which are arranged in respective dimensions; and a recognition unit for outputting a recognition category expressed by a probabilistic model with a highest probability among a plurality of probabilistic models as a recognition result. - View Dependent Claims (12, 13, 14, 15, 16, 17, 18)
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19. An article of manufacture, comprising:
a computer usable medium having computer readable program code means embodied therein for causing a computer to function as a pattern recognition system, the computer readable program code means including; first computer readable program code means for causing said computer to calculate a probability of each probabilistic model expressing features of each recognition category with respect to each input feature vector derived from each input signal, wherein the probabilistic model represents a feature parameter subspace in which feature vectors of each recognition category exist and the feature parameter subspace is expressed by using mixture distributions of one-dimensional discrete distributions with arbitrary distribution shapes which are arranged in respective dimensions; and second computer readable program code means for causing said computer to output a recognition category expressed by a probabilistic model with a highest probability among a plurality of probabilistic models as a recognition result. - View Dependent Claims (20, 21, 22, 23, 24, 25, 26)
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27. A pattern recognition apparatus, comprising:
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an A/D conversion unit for converting input speech signals into digital signals; an input speech feature vector extraction unit for extracting an input speech feature vector from the digital signals converted by the A/D conversion unit; a model training speech data storage unit for storing model training speech data; a model training speech feature vector extraction unit for extracting model training speech feature vectors from the model training speech data stored in the model training speech data storage unit; an initial model generation unit for generating initial models of discrete mixture distribution type models by carrying out a training of continuous mixture distribution type models using the model training speech feature vectors extracted by the model training speech feature vector extraction unit; a model parameter estimation unit for estimating model parameters of the discrete mixture distribution type models using the model training speech feature vectors extracted by the model training speech feature vector extraction unit, with discrete mixture distributions of the initial models generated by the initial model generation unit as initial distributions; a model parameter memory unit for storing the model parameters obtained by the model parameter estimation unit; a model probability calculation unit for calculating a probability of the input speech feature vector with respect to each discrete mixture distribution type model according to the model parameters stored in the model parameter memory unit; and a recognition result output unit for outputting a recognition category expressed by a discrete mixture distribution type model with a highest probability calculated by the model probability calculation unit among the discrete mixture distribution type models as a recognition result.
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28. A pattern recognition method, comprising the steps of:
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(a) converting input speech signals into digital signals; (b) extracting an input speech feature vector from the digital signals converted by the step (a); (c) storing model training speech data; (d) extracting model training speech feature vectors from the model training speech data stored by the step (c); (e) generating initial models of discrete mixture distribution type models by carrying out a training of continuous mixture distribution type models using the model training speech feature vectors extracted by the step (d); (f) estimating model parameters of the discrete mixture distribution type models using the model training speech feature vectors extracted by the step (d), with discrete mixture distributions of the initial models generated by the step (e) as initial distributions; (g) storing the model parameters obtained by the step (f); (h) calculating a probability of the input speech feature vector with respect to each discrete mixture distribution type model according to the model parameters stored by the step (g); and (i) outputting a recognition category expressed by a discrete mixture distribution type model with a highest probability calculated by the step (h) among the discrete mixture distribution type models as a recognition result.
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Specification