Speech recognition method based on neural network stacking autoencoder multi-feature fusion

A stacked autoencoder and multi-feature fusion technology, applied in neural learning methods, biological neural network models, speech recognition, etc. Recognition rate and calculation efficiency, speed up, and the effect of reducing feature dimension

Active Publication Date: 2018-01-19
HANGZHOU DIANZI UNIV
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Problems solved by technology

However, this method cannot guarantee the irrelevance between each feature, that is, there is redundancy in the feature vector obtained by direct splicing, which m

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  • Speech recognition method based on neural network stacking autoencoder multi-feature fusion
  • Speech recognition method based on neural network stacking autoencoder multi-feature fusion
  • Speech recognition method based on neural network stacking autoencoder multi-feature fusion

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Embodiment Construction

[0038] Taking four types of excavation equipment (including hand-held electric picks, excavators, cutters, and hydraulic impact hammers) as examples, the Linear Prediction Cepstrum Coefficients (LPCC) and Mel frequency cepstral coefficients (Mel Frequency Cepstrum Coefficients, MFCC) these two feature extraction methods, the present invention is described further. The following description is only for demonstration and explanation, and does not limit the present invention in any form.

[0039] Model training:

[0040] Step 1. Carry out frame-based windowing on the collected sound data of the four types of excavation equipment during operation. The frame length is N, and the frame shift is Add the Hamming window to get the sound database;

[0041] Step 2, use the LPCC feature extraction algorithm to perform feature extraction on the sound source data of each frame, where the order of LPCC (i.e. the number of LPCC features) we record as R LPCC .

[0042] Step 3. Use the MFC...

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Abstract

The invention relates to a speech recognition method based on neural network stacking autoencoder multi-feature fusion. Firstly, the original sound data is framed and windowed, and the typical time-domain linear predictive cepstrum coefficient feature and the frequency-domain Mel frequency cepstrum coefficient feature are respectively extracted from the framed and windowed data; then the extractedfeatures are spliced, the initial feature representation vector of acoustic signals is constructed and the training feature library is created; then the multi-layer neural network stacking autoencoder is used for feature fusion and learning; the multi-layer autoencoder adopts the over-limit learning machine algorithm to learn training; and finally the extracted features are trained using the over-limit learning machine algorithm to get the classifier model; and the constructed model is finally used to test sample classification and identification. The method adopts multi-feature fusion basedon the over-limit learning machine multi-layer neural network stacking autoencoder, which has higher recognition accuracy compared with the traditional single feature extraction method.

Description

technical field [0001] The invention relates to the technical field of voice recognition, in particular to a voice recognition method based on neural network stacked autoencoder multi-feature fusion. Background technique [0002] Sound recognition is one of the goals of artificial intelligence, and the ability to accurately identify and classify sound signals plays a key role in the development of artificial intelligence. In the existing technology, the traditional method of feature extraction plus classifier has been widely used. However, the feature extraction should be selected according to different types of sounds. Since the source of sound signals is different, the method of feature extraction is also different. It is necessary to select a feature extraction method with corresponding professional knowledge for different sounds. In addition, the sound situation in some environments is more complicated, and the traditional feature extraction methods cannot achieve the d...

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Application Information

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IPC IPC(8): G10L15/02G10L15/06G10L15/14G10L15/20G10L25/24G10L25/30G06N3/08
Inventor 曹九稳程飞王建中
Owner HANGZHOU DIANZI UNIV
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