Exploring wav2vec 2.0 on speaker verification and language identification
| Autor: | Meng Li, Bo Xu, Shiyu Zhou, Zhiyun Fan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
FOS: Computer and information sciences
Sound (cs.SD) Speaker verification Computer Science - Computation and Language Language identification Computer science Process (engineering) Speech recognition Word error rate Computer Science - Sound Resource (project management) Audio and Speech Processing (eess.AS) FOS: Electrical engineering electronic engineering information engineering Computation and Language (cs.CL) Feature learning Electrical Engineering and Systems Science - Audio and Speech Processing |
| Popis: | Wav2vec 2.0 is a recently proposed self-supervised framework for speech representation learning. It follows a two-stage training process of pre-training and fine-tuning, and performs well in speech recognition tasks especially ultra-low resource cases. In this work, we attempt to extend self-supervised framework to speaker verification and language identification. First, we use some preliminary experiments to indicate that wav2vec 2.0 can capture the information about the speaker and language. Then we demonstrate the effectiveness of wav2vec 2.0 on the two tasks respectively. For speaker verification, we obtain a new state-of-the-art result, Equal Error Rate (EER) of 3.61% on the VoxCeleb1 dataset. For language identification, we obtain an EER of 12.02% on 1 second condition and an EER of 3.47% on full-length condition of the AP17-OLR dataset. Finally, we utilize one model to achieve the unified modeling by the multi-task learning for the two tasks. Self-supervised, speaker verification, language identification, multi-task learning, wav2vec 2.0 |
| Databáze: | OpenAIRE |
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