Multichannel speech recognition acoustic modeling method and device based on spatial feature compensation

Abstract

The invention relates to a multichannel speech recognition acoustic modeling method and device based on spatial feature compensation. The model is based on a traditional mixing acoustic modeling frame, namely, a neural network acoustic model, the state posterior probability of a Hidden Markov Model is predicted, and the method comprises the steps that the acoustic feature of speech signals recorded by each single channel in a microphone array and space information features in the microphone array are extracted; the acoustic feature and the space information features are input into the neural network acoustic model to be trained; the predicted acoustic state posterior probability is output by the neural network acoustic model, an acoustic model optimization criterion is used for carrying out iterative updating on neural network parameters, and a multichannel speech recognition acoustic model based on spatial feature compensation is generated. According to the method, a second-best solution caused by separate optimization of the front end and the rear end in a traditional method is avoided; space information provided by the microphone array is effectively utilized by the neural network acoustic model, and the acoustic modeling capacity on multichannel speech signals is improved.

Description

technical field [0001] The invention relates to the field of speech recognition, in particular to an acoustic modeling method and device for multi-channel speech recognition based on spatial feature compensation. Background technique [0002] In recent years, acoustic modeling methods based on Deep Neural Network (DNN) have achieved breakthrough results in the field of speech recognition. The introduction of complex neural networks such as Long Short-Term Memory (LSTM) has further improved the ability of acoustic modeling. However, far-field speech recognition tasks are still challenging due to background noise, reverberation, and human voice interference. [0003] Data recorded by multiple microphones can provide additional spatial information compared to single-microphone acquisition of speech signals. Therefore, a microphone array is usually used to improve the recognition accuracy of far-field speech signals. Traditional multi-channel speech recognition systems genera...

AI Technical Summary

Problems solved by technology

Although studies have shown that DNN can directly use multi-channel output in parallel as network input to model the acoustic state posterior probability, this method still does not make full use of the spatial information introduced by the microphone array, that is, the speaker's position information

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