UniSpeech SAT Base for Speaker Verification Microsoft's UniSpeech The model was pretrained on 16kHz sampled speech audio with utterance and speaker contrastive loss. When using the model, make sure that your speech input is also sampled at 16kHz. The model was pre trained on: 60,000 hours of Libri Light 10,000 hours of GigaSpeech 24,000 hours of VoxPopuli Paper: UNISPEECH SAT: UNIVERSAL SPEECH REPRESENTATION LEARNING WITH SPEAKER AWARE PRE TRAINING Authors: Sanyuan Chen, Yu Wu, Chengyi Wang, Zhengyang Chen, Zhuo Chen, Shujie Liu, Jian Wu, Yao Qian, Furu Wei, Jinyu Li, Xiangzhan Yu Abstract Self supervised learning (SSL) is a long standing goal for speech processing, since it utilizes large scale unlabeled data and avoids extensive human labeling. Recent years witness great successes in applying self supervised learning in speech recognition, while limited exploration was attempted in applying SSL for modeling speaker characteristics. In this paper, we aim to improve the existing SSL framework for speaker representation learning. Two methods are introduced for enhancing the unsupervised speaker information extraction. First, we apply the multi task learning to the current SSL framew…
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