Hiaden Markov model edge decipher data reconstitution method f speech sound identification

A Hidden Markov and Data Reconstruction technology, used in speech recognition, speech analysis, instrumentation, etc.

Inactive Publication Date: 2004-02-18
INST OF ACOUSTICS CHINESE ACAD OF SCI
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  • Hiaden Markov model edge decipher data reconstitution method f speech sound identification
  • Hiaden Markov model edge decipher data reconstitution method f speech sound identification
  • Hiaden Markov model edge decipher data reconstitution method f speech sound identification

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[0036] The human ear's perception of sound has obvious nonlinear characteristics. Incorporating some factors that reflect the auditory characteristics of the human ear into the speech features can significantly improve the performance of the speech recognition system. Considering the critical band effect of the auditory system, the US frequency domain is usually used The triangular filter bank uniformly distributed on the upper part is used to analyze the subband characteristics of the speech feature vector, which has been widely used in speech recognition technology. In the following, the data reconstruction algorithm based on Hidden Markov Model Marginalized Viterbi decoding will be described by taking the data reconstruction of the subband eigenvector of Speech Beauty (Mel) as an example.

[0037] After missing component estimation, the speech feature S is divided into two vectors: the "missing vector" S m and "reliable vector" S o , figure 1 The missing component estimat...

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Abstract

A method for reconfiguring the marginalized decode data of hidden Markovian model (HMM) used in speed recognition features that the HMM transfer probability array is used to describe the dynamic characteristics of speech characteristic vector in time domain, the complete variance array is used to describe the relative characteristics between the components of the characteristic vector for Meizi band, and a data reconfiguring algorithm (VITDI) is used to reconfigured "lost vector". It can improve the noise robustness of speech recognition system.

Description

technical field [0001] The method of the invention relates to the application technology of computer technology, especially in the speech recognition technology, according to the speech feature not covered by the noise, the technology of estimating the speech feature damaged by the noise by using the marginalized Viterbi decoding process. Background technique [0002] Noise robustness is one of the main challenges that speech recognition technology is currently facing. In-depth research on the robustness of speech recognition based on data reconstruction has important theoretical significance and wide application value. [0003] When two sounds of unequal loudness act on the human ear, the presence of the louder frequency component will affect the perception of the lower loudness frequency component, making it less perceptible. This phenomenon is called the masking effect. According to the masking effect of the human ear, a data reconstruction method is proposed. The data r...

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Application Information

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IPC IPC(8): G10L15/14G10L15/20
Inventor 杜利民罗宇
Owner INST OF ACOUSTICS CHINESE ACAD OF SCI
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