Short utterance compensation in speaker verification via cosine-based teacher-student learning of speaker embeddings
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Input utterance with short duration is one of the most critical threats that degrade the performance of speaker verification systems. This study aimed to develop an integrated text-independent speaker verification system that inputs utterances with short durations of 2.05 seconds. For this goal, we propose an approach using a teacher-student learning framework that maximizes the cosine similarity of two speaker embeddings extracted from long and short utterances. In the proposed architecture, phonetic-level features in which each feature represents a segment of 130 ms are extracted using convolutional layers. The gated recurrent units extract an utterance-level speaker embedding using the phonetic-level features. Experiments were conducted using deep neural networks that take raw waveforms as input, and output speaker embeddings on the VoxCeleb 1 dataset. The equal error rates without short utterance compensation are 8.72 respectively. The proposed model with compensation exhibits an equal error rate of 10.08 performance degradation.
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