Voice analyzing system using hidden Markov model and having plural neural network predictors

• US 5,307,444 A
• Filed: 12/12/1990
• Issued: 04/26/1994
• Est. Priority Date: 12/12/1989
• Status: Expired due to Fees

• Current Assignee: Matsushita Electric Industrial Company Limited (Panasonic Holdings Corporation)
• Original Assignee: Matsushita Electric Industrial Company Limited (Panasonic Holdings Corporation)
• Inventors: Tsuboka, Eiichi
• Primary Examiner(s): MacDonald, Allen R.
• Assistant Examiner(s): DAVIS, GEORGE B
• Application Number:
  US07/626,300
• Time in Patent Office: 1,231 Days
• Field of Search: 364/513, 364/513.5, 364/807, 395/22, 395/2, 395/23, 381/43
• US Class Current: 706/20
• CPC Class Codes: G10L 15/142 Hidden Markov Models [HMMs]
• CPC Class Codes: G10L 25/30 using neural networks

Abstract

An analyzing system analyzes object signals, particularly voice signals, by estimating a generation likelihood of an observation vector sequence being a time series of feature vectors with use of a Markov model having a plurality of states and given transition probabilities from state to state. A state designation section determines a state i at a time t stochastically using the Markov model. Plural predictors, each of which is composed of a neural network and is provided per each state of the Markov model, are provided for generating a predictional vector of the feature vector x_t in the state i at the time t based on values of the feature vectors other than the feature vector x_t. A first calculation section calculates an error vector of the predictional vector to the feature vector x_T. A second calculation section calculates a generation likelihood of the error vector using a predetermined probability distribution of the error vector according to which the error vector is generated.

Claims

1. An analyzing system for analyzing object signals, comprising voice signals, by estimating a generation likelihood of an observation vector sequence being a time series of feature vectors X (=x₁, . . . , x_T ;

T is a total number of frames) with use of a Markov model having a plurality of states i (i=1, . . . , N;

N is a total number of states) and given transition probabilities from state i to state j (i, j=1, . . . , N), comprising;

feature extraction means for converting the object signals into the time series of feature vectors X;

a state designation means for determining a state i at a time t stochastically using said Markov model;

a plurality of predictors each of which is composed of a neural network and is provided per each state of said Markov model for generating a predictional vector g_i (t) of said feature vector x_t in said state i at the time t based on values of the feature vectors other than said feature vector x_t ;

a first calculation means for calculating an error vector of said predictional vector g_i (t) to said feature vector x_T ; and

a second calculation means for calculating a generation likelihood of said error vector using a predetermined probability distribution of the error vector according to which said error vector is generated.

2. The analyzing system as claimed in claim 1 in which said predictor is comprised of a neural network having an input layer, at least one hidden layer and an output layer, said neural network being constructed so as to output a predictional vector of a feature vector when other feature vectors are input to said input layer.

3. The analyzing system as claimed in claim 2 in which each respective layer of said neural network is made up of units having a non-linear input to output characteristic.

4. The analyzing system as claimed in claim 1 wherein parameters used for defining said Markov model and respective predictors are determined based on a parameter estimation learning sequence wherein a set of training observation vector sequences are provided and renewal of said parameters is repeated until a generation likelihood for said training observation vector sequences estimated by the analyzing system becomes a maximum.

5. A recognition system for recognizing object signals comprising voice signals, comprising:

a plurality of analyzing apparatuses each for estimating a generation likelihood of an observation vector sequence being a time series of feature vectors X (=x_t, . . . , x_t ;

t is a total number of frames) with use of a Markov model having a plurality of states i (i=1, . . . , N;

N is a total number of states) and given transition probabilities from state i to state j (i, j=1, . . . , N);

feature extraction means for converting the object signals into the time series of feature vectors X;

said each of analyzing apparatuses comprising a state designation means for determining a state i at a time t stochastically using said Markov model, a plurality of predictors each of which is composed of a neural network and is provided per each state of said Markov model for generating a predictional vector g_i (t) of said feature vector x_t in said state i at the time t based on values of the feature vectors other than said feature vector x_t, a first calculation means for calculating an error vector of said predictional vector g_i (t) to said feature vector x_t, and a second calculation means for calculating a generation likelihood of said error vector using a predetermined probability distribution of the error vector according to which said error vector is generated, and being adapted for a category for an observation vector sequence to be categorized;

a maximum likelihood detection means which compares likelihoods obtained by said plurality of analyzing apparatuses and detects a maximum value among said likelihoods; and

a decision means for identifying said observation vector sequence to the category corresponding to one of said plurality of analyzing apparatuses which gives the maximum likelihood detected by said maximum likelihood detection means.