Speaker-conditioned phrasing with phoneme-level PLMs (MP BERT) improves pause prediction from F0.5 0.3719 to 0.4991, and a few-shot adapter generalizes to unseen speakers.
SpeakerNet: 1D Depth-wise Separable Convolutional Network for Text-Independent Speaker Recognition and Verification
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abstract
We propose SpeakerNet - a new neural architecture for speaker recognition and speaker verification tasks. It is composed of residual blocks with 1D depth-wise separable convolutions, batch-normalization, and ReLU layers. This architecture uses x-vector based statistics pooling layer to map variable-length utterances to a fixed-length embedding (q-vector). SpeakerNet-M is a simple lightweight model with just 5M parameters. It doesn't use voice activity detection (VAD) and achieves close to state-of-the-art performance scoring an Equal Error Rate (EER) of 2.10% on the VoxCeleb1 cleaned and 2.29% on the VoxCeleb1 trial files.
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Speaker-Conditioned Phrase Break Prediction for Text-to-Speech with Phoneme-Level Pre-trained Language Model
Speaker-conditioned phrasing with phoneme-level PLMs (MP BERT) improves pause prediction from F0.5 0.3719 to 0.4991, and a few-shot adapter generalizes to unseen speakers.