MSRN and Multi-Headed Attention Mechanism for Language Identification

With the popularity of the mobile internet, people all over the world can easily create and publish diverse media content such as multilingual and multi-dialectal audio and video. Therefore, language or dialect identification (LID) is increasingly important for practical applications such as multilingual and cross lingual processing as the front-end part of the subsequent tasks such as speech recognition and voice identification. This paper proposes a neural network framework based on a multiscale residual network (MSRN) and multi-headed self-attention (MHSA). Experimental results show that this method can effectively improve the accuracy and robustness compared to other methods. This model uses the MSRN to extract the language spectrogram feature and uses MHSA to filter useful features and suppress irrelevant features. Training and test sets are constructed from both the “Common Voice” and “Oriental Language Recognition” (AP17-OLR) datasets. The experimental results show that this model can effectively improve the accuracy and robustness of LID.