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Improving Voice Separation by Incorporating End-to-End Speech Recognition

Academic Conference
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Naoya Takahashi
Mayank Kumar Singh
Sakya Basak
Sriram Ganapathy
Yuki Mitsufuji (Sony Corporation)
Research Areas
Audio & Acoustics


Despite recent advances in voice separation methods, many challenges remain in realistic scenarios such as noisy recording and the limits of available data. In this work, we propose to explicitly incorporate the phonetic and linguistic nature of speech by taking a transfer learning approach using an end-to-end automatic speech recognition (E2EASR) system. The voice separation is conditioned on deep features extracted from E2EASR to cover the long-term dependence of phonetic aspects. Experimental results on speech separation and enhancement task on the AVSpeech dataset show that the proposed method significantly improves the signal-to-distortion ratio over the baseline model and even outperforms an audio visual model, that utilizes visual information of lip movements.

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