Model combination type speech recognition method based on GMM (Gaussian mixture model) noise estimation

A noise estimation and speech recognition technology, which is applied in speech recognition, speech analysis, instruments, etc., can solve the problems of real-time system acceptance, application range limitation, and noise estimation inability to achieve the effect of saving power and prolonging battery life
CN105355199AInactive Publication Date: 2016-02-24HOHAI UNIV
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Patent Information

Authority / Receiving Office
CN ยท China
Patent Type
Applications(China)
Current Assignee / Owner
HOHAI UNIV
Publication Date
2016-02-24
Estimated Expiration
Not applicable ยท inactive patent

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Abstract

The invention discloses a model combination type speech recognition method based on GMM (Gaussian mixture model) noise estimation. According to the method, a GMM containing fewer Gaussian units is used for real-time estimation of noise parameters in noisy testing speech and monitoring change of noise. The noise parameters are estimated according to specific time intervals and are updated once at every time interval, and mute segments are processed as noisy speech. Except for use for model combination, the estimated noise parameters are stored in an internal storage to be used for making noise change judgment of next time interval. The noise monitoring includes firstly, reading the noise parameters of last time interval from the storage; then, combining the noise parameters with a clean speech GMM so as to obtain a noisy speech GMM, subjecting noisy testing speech of current time interval to probability calculation, comparing an output average log likelihood value with an average log likelihood value outputted by a noise parameter estimation submodule, considering that noise changes if the likelihood value is larger than a threshold value, and considering that noise is unchanged if not.
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Description

technical field

[0001] The invention relates to a model-combined speech recognition method based on GMM noise estimation. Specifically, the noise parameters extracted in the test environment are used to adjust the parameters of the acoustic model of the speech recognition system to match the noise-containing speech feature parameters extracted in the actual environment. , a model combination method for improving system noise robustness; it belongs to the technical field of speech recognition. Background technique

[0002] Automatic speech recognition technology can provide convenient input interfaces for electronic devices, and has been widely used in mobile devices such as mobile phones, tablet computers, and navigators. However, in practical applications, speech variability such as environmental noise is inevitable, which usually leads to a sharp decline in the performance of the speech recognition system, so it is necessary to take measures to improve the environmental ro...

Claims

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