Speech recognition method and device and electronic equipment

Abstract

The embodiment of the invention provides a voice recognition method and device and electronic equipment, and belongs to the technical field of data processing. The method comprises the steps: randomlyinitializing and training an LSTM network model through a preset training sample; based on a set first training parameter related to a CTC loss function and a set second training parameter related toKL divergence, forming a loss function for training a BLSTM network by using a training result of the LSTM network; in the process of training the BLSTM network, gradually reducing the value of the second training parameter while gradually increasing the value of the first training parameter; and when the performance index output by the BLSTM network meets a preset requirement, stopping the training of the BLSTM network so as to carry out real-time character prediction on an input sound file by utilizing the BLSTM network. Through the processing scheme of the invention, the timestamp accuracyof acoustic network model prediction can be improved.

Description

technical field [0001] The present disclosure relates to the technical field of data processing, and in particular to an acoustic network model training method, device and electronic equipment. Background technique [0002] Speech processing (Speech processing), also known as voice signal processing, human voice processing, its purpose is to make the desired signal, further do voice recognition, apply to the mobile phone interface and even ordinary life, so that people and computers can communicate. [0003] In the process of speech processing, the audio-like sound signal received by a microphone or other devices can be used to process the data through an analog-to-digital conversion device, and finally output through a digital-to-analog conversion device. Therefore, the processing is for digital signals, and the speech signal is a discrete time signal. The signal processing flow is as follows: Collect and sample signals: use a microphone or various radio devices to collect...

AI Technical Summary

Problems solved by technology

The signal processing flow is as follows: Collect and sample signals: use a microphone or various radio devices to collect analog voice signals, and then use an ADC device (such as an analog-to-digital conversion card) to convert the analog signal into a digital signal, and then according to the Nyquist theory For sampling, if it does not conform to the theory, it will cause signal distortion

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