Voice processing model training method and device, equipment and storage medium

Abstract

The invention provides a voice processing model training method and device, equipment and a computer readable storage medium. The method comprises the steps: obtaining sample data comprising voice of a source language and a target language sample text corresponding to the voice of the source language; inputting the voice of the source language into a voice recognition sub-model of a voice processing model to obtain a source language text; inputting the source language text into the word database of the voice processing model for traversal to obtain a word vector corresponding to the source language text; inputting the word vector and the target language sample text into the machine translation sub-model of the voice processing model to obtain a target language translated text; based on a preset loss function, calculating a loss value of the voice processing model according to the target language translation text and the target language sample text; and performing parameter adjustment on the voice processing model according to the loss value to obtain a trained voice processing model. According to the invention, the training data of the model can be reduced, and the training efficiency is improved. The invention further relates to a block chain technology.

Description

technical field [0001] The present application relates to the field of computer technology, and in particular to a training method, device, equipment and computer-readable storage medium for a speech processing model. Background technique [0002] At present, most of the speech translation technologies first transcribe the text of the speech through ASR, and then translate the transcribed text into the required target text through machine translation, which needs to be transcribed by the automatic speech recognition (ASR) model and the neural machine translation (NMT) model Translation, when training the above model, a large amount of data is required, the training efficiency is not high, and the transcription effect of the automatic speech recognition (ASR) model is not accurate enough, the output result after translation processing will produce a larger errors, resulting in speech-to-text translation results that are not as expected. Contents of the invention [0003] T...

AI Technical Summary

Problems solved by technology

[0002] At present, most of the speech translation technologies first transcribe the text of the speech through ASR, and then translate the transcribed text into the required target text through machine translation, which needs to be transcribed by the automatic speech recognition (ASR) model and the neural machine translation (NMT) model Translation, when training the above model, a large amount of data is required, the training efficiency is not high, and the transcription effect of the automatic speech recognition (ASR) model is not accurate enough, the output result after translation processing will produce a larger error, resulting in speech-to-text translation results that are not as expected

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