Speech recognition model training method, speech recognition method and related devices

Abstract

The embodiment of the invention provides a speech recognition model training method, a speech recognition method and related devices. The training method comprises the steps of: determining a trainingcurrent mixed language audio, obtaining the training initial acoustic features, utilizing a first language module to obtain a training first time sequence position acoustic feature, utilizing a second language module to obtain a training second time sequence position acoustic feature, performing fusion and text coding on the training first time sequence position acoustic feature and the trainingsecond time sequence position acoustic feature to obtain a training current fusion text feature, obtaining a first training current prediction text feature according to the training current fusion text feature and a previous reference text feature, obtaining first loss according to the first training current prediction text feature and a current reference text feature, then obtaining model loss, and adjusting the parameters of a speech recognition model according to the model loss until the trained speech recognition model is obtained. According to the voice recognition model training method,the voice recognition method and the related devices provided by the embodiment of the invention, the voice recognition accuracy can be improved.

Description

technical field [0001] The embodiments of the present invention relate to the field of computers, and in particular, to a speech recognition model training method, a speech recognition method and related devices. Background technique [0002] With the development of computer technology and deep learning technology, speech recognition technology has become an important research direction and has been widely used. [0003] However, in some scenarios, at least two types of languages ​​will exist at the same time. In order to recognize the speech and audio of multiple types of languages ​​at the same time, a speech recognition algorithm can be used to construct a pronunciation dictionary. For example, for Chinese English mixed audio, first establish the English pronunciation to Chinese pronunciation, and construct the pronunciation vocabulary according to the Chinese pronunciation phoneme, this method can solve some Chinese and English mixed recognition situations, the performan...

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Problems solved by technology

[0003] However, in some scenarios, at least two types of languages ​​will exist at the same time. In order to recognize the speech and audio of multiple types of languages ​​at the same time, a speech recognition algorithm can be used to construct a pronunciation dictionary. For example, for Chinese English mixed audio, first establish the English pronunciation to Chinese pronunciation, and construct the pronunciation vocabulary according to the Chinese pronunciation phoneme, this method can solve some Chinese and English mixed recognition situations, the performance mainly depends on the English word mapping to the Chinese pronunciation vocabulary size, but this process not only requires manual labeling, but also many English words have similar pronunciations to Chinese words or cannot be mapped to Chinese pronunciations, so this method has poor generalization and it is difficult to obtain good speech recognition results; you can also use The method is based on the deep neural network model, but the training of the model requires a large amount of labeled data, and the amount of data mixed with multiple types of languages ​​is small, the model cannot be well trained, and the effect of speech recognition is also poor.

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