Raw waveform speaker verification for supervised and self-supervised learning

• URL: http://arxiv.org/abs/2203.08488v1
• Date: Wed, 16 Mar 2022 09:28:03 GMT
• Title: Raw waveform speaker verification for supervised and self-supervised learning
• Authors: Jee-weon Jung, You Jin Kim, Hee-Soo Heo, Bong-Jin Lee, Youngki Kwon, Joon Son Chung
• Abstract summary: This paper proposes a new raw waveform speaker verification model that incorporates techniques proven effective for speaker verification. Under the best performing configuration, the model shows an equal error rate of 0.89%, competitive with state-of-the-art models. We also explore the proposed model with a self-supervised learning framework and show the state-of-the-art performance in this line of research.
• Score: 30.08242210230669
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Speaker verification models that directly operate upon raw waveforms are receiving growing attention. However, their performances are less competitive than the state-of-the-art handcrafted feature-based counterparts, demonstrating equal error rates under 1% on the benchmark VoxCeleb1 evaluation protocol. In addition, they have yet not been explored with self-supervised learning frameworks. This paper proposes a new raw waveform speaker verification model that incorporates techniques proven effective for speaker verification, including the Res2Net backbone module and the aggregation method considering both context and channels. Under the best performing configuration, the model shows an equal error rate of 0.89%, competitive with state-of-the-art models. We also explore the proposed model with a self-supervised learning framework and show the state-of-the-art performance in this line of research. Finally, we show that leveraging the model trained with self-supervision successfully serves as a pre-trained model under the semi-supervised scenario where it is assumed that only a limited amount of data has a ground truth label and a bigger data has no label.

Related papers

• Evaluation of Speech Representations for MOS prediction [0.7329200485567826]
  In this paper, we evaluate feature extraction models for predicting speech quality. We also propose a model architecture to compare embeddings of supervised learning and self-supervised learning models with embeddings of speaker verification models.
  arXiv  Detail & Related papers  (2023-06-16T17:21:42Z)
• How to Estimate Model Transferability of Pre-Trained Speech Models? [84.11085139766108]
  "Score-based assessment" framework for estimating transferability of pre-trained speech models. We leverage upon two representation theories, Bayesian likelihood estimation and optimal transport, to generate rank scores for the PSM candidates. Our framework efficiently computes transferability scores without actual fine-tuning of candidate models or layers.
  arXiv  Detail & Related papers  (2023-06-01T04:52:26Z)
• Debiasing Vision-Language Models via Biased Prompts [79.04467131711775]
  We propose a general approach for debiasing vision-language foundation models by projecting out biased directions in the text embedding. We show that debiasing only the text embedding with a calibrated projection matrix suffices to yield robust classifiers and fair generative models.
  arXiv  Detail & Related papers  (2023-01-31T20:09:33Z)
• Language Models in the Loop: Incorporating Prompting into Weak Supervision [11.10422546502386]
  We propose a new strategy for applying large pre-trained language models to novel tasks when labeled training data is limited. Instead of applying the model in a typical zero-shot or few-shot fashion, we treat the model as the basis for labeling functions in a weak supervision framework.
  arXiv  Detail & Related papers  (2022-05-04T20:42:40Z)
• Self-supervised Speaker Diarization [19.111219197011355]
  This study proposes an entirely unsupervised deep-learning model for speaker diarization. Speaker embeddings are represented by an encoder trained in a self-supervised fashion using pairs of adjacent segments assumed to be of the same speaker.
  arXiv  Detail & Related papers  (2022-04-08T16:27:14Z)
• Explain, Edit, and Understand: Rethinking User Study Design for Evaluating Model Explanations [97.91630330328815]
  We conduct a crowdsourcing study, where participants interact with deception detection models that have been trained to distinguish between genuine and fake hotel reviews. We observe that for a linear bag-of-words model, participants with access to the feature coefficients during training are able to cause a larger reduction in model confidence in the testing phase when compared to the no-explanation control.
  arXiv  Detail & Related papers  (2021-12-17T18:29:56Z)
• Improving Label Quality by Jointly Modeling Items and Annotators [68.8204255655161]
  We propose a fully Bayesian framework for learning ground truth labels from noisy annotators. Our framework ensures scalability by factoring a generative, Bayesian soft clustering model over label distributions into the classic David and Skene joint annotator-data model.
  arXiv  Detail & Related papers  (2021-06-20T02:15:20Z)
• A Multi-Level Attention Model for Evidence-Based Fact Checking [58.95413968110558]
  We present a simple model that can be trained on sequence structures. Results on a large-scale dataset for Fact Extraction and VERification show that our model outperforms the graph-based approaches.
  arXiv  Detail & Related papers  (2021-06-02T05:40:12Z)
• Self-supervised Text-independent Speaker Verification using Prototypical Momentum Contrastive Learning [58.14807331265752]
  We show that better speaker embeddings can be learned by momentum contrastive learning. We generalize the self-supervised framework to a semi-supervised scenario where only a small portion of the data is labeled.
  arXiv  Detail & Related papers  (2020-12-13T23:23:39Z)
• Self-Supervised Contrastive Learning for Unsupervised Phoneme Segmentation [37.054709598792165]
  The model is a convolutional neural network that operates directly on the raw waveform. It is optimized to identify spectral changes in the signal using the Noise-Contrastive Estimation principle. At test time, a peak detection algorithm is applied over the model outputs to produce the final boundaries.
  arXiv  Detail & Related papers  (2020-07-27T12:10:21Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.