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

Inactive Publication Date: 2006-08-16
LG ELECTRONICS INC
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Problems solved by technology

However, a limitation of these methods is that they do not take into account the difference between the voiced and non-voiced sounds that make up the speech

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  • Method for extracting feature vectors for speech recognition
  • Method for extracting feature vectors for speech recognition
  • Method for extracting feature vectors for speech recognition

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Embodiment Construction

[0029] Reference will now be made in detail to the preferred embodiments of the invention, examples of which are illustrated in the accompanying drawings.

[0030] A method of the present invention involves generating a parameter based on a determination of whether a sound is voiced or unvoiced, and using this parameter, along with feature vectors relating to the complete spectral shape of the speech, during the training phase and the recognition phase. The method will be realized using a computer program stored in a recording medium such as but not limited to a memory.

[0031] Human speech includes voiced and unvoiced sounds. Voiced sounds are produced when the vocal cords vibrate during speaking, while non-voiced sounds are produced when the vocal cords are not vibrating.

[0032] All vowels and the plosives [b], [d], and [g] are voice sounds. Whereas plosives [k], [p] and [t] and fricatives [f], [th], [s] and [sh] are non-speech sounds. Although plosives [p] and [b] (an...

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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.

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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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G10L15/02G10L15/00G10L15/08
CPCG10L15/02G10L25/93
Inventor 金灿佑
Owner LG ELECTRONICS INC
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