Phoneme-aware and Channel-wise Attentive Learning for Text DependentSpeaker Verification

• URL: http://arxiv.org/abs/2106.13514v1
• Date: Fri, 25 Jun 2021 09:11:18 GMT
• Title: Phoneme-aware and Channel-wise Attentive Learning for Text DependentSpeaker Verification
• Authors: Yan Liu, Zheng Li, Lin Li, Qingyang Hong
• Abstract summary: This paper proposes a multi-task learning network with phoneme-aware and channel-wise attentive learning strategies for text-dependent Speaker Verification (SV) The proposed system achieves outstanding results for textdependent SV.
• Score: 21.826585075806573
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper proposes a multi-task learning network with phoneme-aware and channel-wise attentive learning strategies for text-dependent Speaker Verification (SV). In the proposed structure, the frame-level multi-task learning along with the segment-level adversarial learning is adopted for speaker embedding extraction. The phoneme-aware attentive pooling is exploited on frame-level features in the main network for speaker classifier, with the corresponding posterior probability for the phoneme distribution in the auxiliary subnet. Further, the introduction of Squeeze and Excitation (SE-block) performs dynamic channel-wise feature recalibration, which improves the representational ability. The proposed method exploits speaker idiosyncrasies associated with pass-phrases, and is further improved by the phoneme-aware attentive pooling and SE-block from temporal and channel-wise aspects, respectively. The experiments conducted on RSR2015 Part 1 database confirm that the proposed system achieves outstanding results for textdependent SV.

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