Method for extracting feature vectors for speech recognition

A recognition method and eigenvector technology, applied in speech recognition, speech analysis, instruments, etc., can solve problems such as not considering
CN1819017AInactive Publication Date: 2006-08-16LG ELECTRONICS INC
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Applications(China)
Current Assignee / Owner
LG ELECTRONICS INC
Publication Date
2006-08-16
Estimated Expiration
Not applicable ยท inactive patent

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Abstract

Disclosed is a method for speech recognition which achieves a high recognition rate. The method includes extracting a parameter from an input signal that represents a characterization of the input signal as a voiced or unvoiced sound, extracting at least one feature vector corresponding to an overall spectrum shape of a voice from an input signal, and using the extracted parameter and extracted feature vectors in a training phase and in a recognition phase to recognize speech.
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Description

technical field

[0001] The invention relates to speech recognition, in particular to a method for extracting feature vectors to achieve high speech recognition rate. Background technique

[0002] In the neighborhood of speech recognition, the two speech recognition methods that are mainly used are Hidden Markov Model (HMM) and Dynamic Time Warping (DTW).

[0003] In the HMM-based speech recognition method, HMM parameters are acquired in the training stage and stored in a speech database, and a Markov processor searches for a model with the highest recognition rate using a maximum likelihood (ML) method. Feature vectors necessary for speech recognition are extracted, and training and speech recognition are performed using the extracted feature vectors.

[0004] In the training phase, the HMM parameters are usually obtained using the Expectation Maximum (EM) algorithm or the Baum-Welch re-estimation algorithm. The Viterbi algorithm is usually used in the speech recognition s...

Claims

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