2023-05-11 更新
Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition
Authors:Dima Rekesh, Samuel Kriman, Somshubra Majumdar, Vahid Noroozi, He Juang, Oleksii Hrinchuk, Ankur Kumar, Boris Ginsburg
Conformer-based models have become the most dominant end-to-end architecture for speech processing tasks. In this work, we propose a carefully redesigned Conformer with a new down-sampling schema. The proposed model, named Fast Conformer, is 2.8x faster than original Conformer, while preserving state-of-the-art accuracy on Automatic Speech Recognition benchmarks. Also we replace the original Conformer global attention with limited context attention post-training to enable transcription of an hour-long audio. We further improve long-form speech transcription by adding a global token. Fast Conformer combined with a Transformer decoder also outperforms the original Conformer in accuracy and in speed for Speech Translation and Spoken Language Understanding.
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Learning Robust Self-attention Features for Speech Emotion Recognition with Label-adaptive Mixup
Authors:Lei Kang, Lichao Zhang, Dazhi Jiang
Speech Emotion Recognition (SER) is to recognize human emotions in a natural verbal interaction scenario with machines, which is considered as a challenging problem due to the ambiguous human emotions. Despite the recent progress in SER, state-of-the-art models struggle to achieve a satisfactory performance. We propose a self-attention based method with combined use of label-adaptive mixup and center loss. By adapting label probabilities in mixup and fitting center loss to the mixup training scheme, our proposed method achieves a superior performance to the state-of-the-art methods.
PDF Accepted to ICASSP 2023
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