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SpeakerNet: 1D Depth-wise Separable Convolutional Network for Text-Independent Speaker Recognition and Verification

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arxiv 2010.12653 v1 pith:T23WEJKB submitted 2020-10-23 eess.AS

SpeakerNet: 1D Depth-wise Separable Convolutional Network for Text-Independent Speaker Recognition and Verification

Nithin Rao Koluguri , Jason Li , Vitaly Lavrukhin , Boris Ginsburg This is my paper
classification eess.AS
keywords speakerarchitecturedepth-wiserecognitionseparablespeakernetverificationvoxceleb1
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score.

  1. Speaker-Conditioned Phrase Break Prediction for Text-to-Speech with Phoneme-Level Pre-trained Language Model

    eess.AS 2025-08 conditional novelty 5.0

    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.

  2. A Framework for Robust Speaker Verification in Highly Noisy Environments Leveraging Both Noisy and Enhanced Audio

    eess.AS 2025-08 conditional novelty 4.0

    A Siamese MLP that fuses speaker embeddings from noisy and DeepFilterNet-enhanced speech cuts speaker verification error at SNR -10 dB and below, while degrading performance near 0 dB.