Accent classification method based on deep neural network and model thereof

Abstract

The invention discloses an accent classification method based on a deep neural network. The method comprises the steps: extracting frame-level frequency domain features of an original audio, and constructing a 2D speech spectrum as a network input X; constructing a multi-task weight shared CRNNs-based front-end encoder to extract local sequence descriptors {P1,..., PT'} of the spectrum X; in the training process, additionally arranging a speech recognition task branch network behind the front-end encoder to inhibit an overfitting phenomenon in accent recognition; constructing a core branch network for an accent recognition task, and firstly, integrating all local sequence descriptors into a global accent feature; secondly, introducing a discriminative loss function in a prediction process; and finally, classifying the global accent features through a softmax-based classification layer to realize accent prediction. The invention also discloses a highly discriminatable accent classification model based on the deep neural network, and reliable accent prediction can be provided for speakers from different regional groups.

Description

technical field [0001] The invention relates to the field of deep learning, in particular to a deep neural network-based accent classification method and its model. Background technique [0002] Accent is the diversity of pronunciation behavior of speakers in a certain language. The different pronunciation methods can be attributed to the speaker's social environment, living area, etc. However, a wide variety of accents can challenge the generalizability of voice-controlled technologies, such as automatic speech recognition. Accent recognition technology can be used to solve accent-related problems or predict the speaker's regional identity, and has been extensively studied in recent years. With the development of deep learning technology, more and more image or voice recognition problems can be well solved by training artificial neural network models. Similar to face recognition, speaker recognition, etc., the core content of accent recognition tasks can be summarized as ...

AI Technical Summary

Problems solved by technology

But accents belong to the pronunciation habits of a group of speakers in the same area, so accent recognition is more challenging than speaker recognition in learning a group-level feature

Especially in the speech scene where more and more speakers tend to standard pronunciation, the detection and recognition of accents is becoming more and more difficult

In addition, because the accent recognition task training process is prone to overfitting phenomenon, which is often caused by inaccurate learning process

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