Emotion Recognition in Lhasa Tibetan Speech based on Bi-LSTM Graph Convolutional Networks

Ang Chen; Rongzhao Huang; Tong Xi; Liang Wu; Wangdui Bianba

doi:10.54097/y9p1va72

Authors

Ang Chen
Rongzhao Huang
Tong Xi
Liang Wu
Wangdui Bianba

DOI:

https://doi.org/10.54097/y9p1va72

Keywords:

Tibetan Speech Emotion Recognition, Mel-frequency Cepstral Coefficients (MFCC), Bidirectional Long Short-Term Memory (Bi-LSTM), Graph Convolution Network (GCN)

Abstract

Speech Emotion Recognition (SER) is a crucial component in the field of Human-Computer Interaction (HCI), with significant research and practical application implications. However, due to the complexity of the Tibetan language and the scarcity of datasets caused by the difficulty in collecting various dialects, there are not many research achievements in Tibetan speech recognition. Based on the foundation of constructing a TBLS1 dataset containing 6,000 Tibetan-language speech samples, an approach was devised for Tibetan speech emotion recognition. This approach leverages MFCC features and incorporates a Bi-directional Long Short-Term Memory (Bi-LSTM) network within a graph convolutional neural network. Finally, by comparing the performance of different models on this dataset, we demonstrated the feasibility of our model for Tibetan speech emotion recognition.

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References

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