Short utterance compensation in speaker verification via cosine-based teacher-student learning of speaker embeddings
This addresses a critical bottleneck in speaker verification for applications with limited audio data, though it is an incremental improvement as it applies an existing framework to a new problem.
The study tackled performance degradation in speaker verification systems caused by short input utterances (2 seconds or less) by proposing a teacher-student learning framework that compensates at the phonetic level within the embedding network, achieving approximately 65% recovery of performance degradation.
The short duration of an input utterance 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 duration of 2 seconds or less. We propose an approach using a teacher-student learning framework for this goal, applied to short utterance compensation for the first time in our knowledge. The core concept of the proposed system is to conduct the compensation throughout the network that extracts the speaker embedding, mainly in phonetic-level, rather than compensating via a separate system after extracting the speaker embedding. In the proposed architecture, phonetic-level features where each feature represents a segment of 130 ms are extracted using convolutional layers. A layer of gated recurrent units extracts an utterance-level feature using phonetic-level features. The proposed approach also adopts a new objective function for teacher-student learning that considers both Kullback-Leibler divergence of output layers and cosine distance of speaker embeddings layers. Experiments were conducted using deep neural networks that take raw waveforms as input, and output speaker embeddings on VoxCeleb1 dataset. The proposed model could compensate approximately 65 \% of the performance degradation due to the shortened duration.