Multilingual model training method based on cross-language self-training

Abstract

The invention provides a multilingual model training method based on cross-language self-training. The method comprises the steps: firstly training an acoustic phoneme classifier on labeled data of a certain high-resource language as a target network, and then training a main network to approach the characterization of the acoustic phoneme classifier in multiple languages. The method specifically comprises: obtaining a target network; training the main network; and migrating the trained main network to the automatic speech recognition model of the target language.

Description

technical field [0001] The invention relates to the field of low-resource speech recognition and speech representation learning, in particular to a multilingual model training method based on cross-language self-training. Background technique [0002] The current advanced Automatic Speech Recognition (ASR) models usually need to be trained on hundreds or thousands of labeled data, and this scale of labeled data is usually difficult to obtain in low-resource languages. The pre-training method can effectively solve the data problem of low-resource ASR. It first pre-trains a model through other resources (data in other languages ​​or unlabeled data in this language), and then migrates the model to the low-resource ASR model. [0003] Existing pre-training methods can be divided into supervised and unsupervised methods. Early research focused on supervised pre-training, in which the pre-trained model is usually trained on labeled data in one or more other languages, and then us...

AI Technical Summary

Problems solved by technology

Since the teacher model cannot decode the speech of other languages, the self-training method can only use the data of this language

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