Adding room dimensions and reverberation time as inputs to a distance-based target speech extraction model improves SDR by about 1.2 dB on unseen simulated and real rooms.
Focus on the Sound around You: Monaural Target Speaker Extraction via Distance and Speaker Information
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abstract
Previously, Target Speaker Extraction (TSE) has yielded outstanding performance in certain application scenarios for speech enhancement and source separation. However, obtaining auxiliary speaker-related information is still challenging in noisy environments with significant reverberation. inspired by the recently proposed distance-based sound separation, we propose the near sound (NS) extractor, which leverages distance information for TSE to reliably extract speaker information without requiring previous speaker enrolment, called speaker embedding self-enrollment (SESE). Full- & sub-band modeling is introduced to enhance our NS-Extractor's adaptability towards environments with significant reverberation. Experimental results on several cross-datasets demonstrate the effectiveness of our improvements and the excellent performance of our proposed NS-Extractor in different application scenarios.
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Single-Channel Target Speech Extraction Utilizing Distance and Room Clues
Adding room dimensions and reverberation time as inputs to a distance-based target speech extraction model improves SDR by about 1.2 dB on unseen simulated and real rooms.