Some voices are too common: Building fair speech recognition systems using the Common Voice dataset

• URL: http://arxiv.org/abs/2306.03773v1
• Date: Thu, 1 Jun 2023 11:42:34 GMT
• Title: Some voices are too common: Building fair speech recognition systems using the Common Voice dataset
• Authors: Lucas Maison, Yannick Est\`eve
• Abstract summary: We use the French Common Voice dataset to quantify the biases of a pre-trained wav2vec2.0 model toward several demographic groups. We also run an in-depth analysis of the Common Voice corpus and identify important shortcomings that should be taken into account.
• Score: 2.28438857884398
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Automatic speech recognition (ASR) systems become increasingly efficient thanks to new advances in neural network training like self-supervised learning. However, they are known to be unfair toward certain groups, for instance, people speaking with an accent. In this work, we use the French Common Voice dataset to quantify the biases of a pre-trained wav2vec~2.0 model toward several demographic groups. By fine-tuning the pre-trained model on a variety of fixed-size, carefully crafted training sets, we demonstrate the importance of speaker diversity. We also run an in-depth analysis of the Common Voice corpus and identify important shortcomings that should be taken into account by users of this dataset.

Related papers

• Pre-Finetuning for Few-Shot Emotional Speech Recognition [20.894029832911617]
  We view speaker adaptation as a few-shot learning problem. We propose pre-finetuning speech models on difficult tasks to distill knowledge into few-shot downstream classification objectives.
  arXiv  Detail & Related papers  (2023-02-24T22:38:54Z)
• Self-Supervised Speech Representation Learning: A Review [105.1545308184483]
  Self-supervised representation learning methods promise a single universal model that would benefit a wide variety of tasks and domains. Speech representation learning is experiencing similar progress in three main categories: generative, contrastive, and predictive methods. This review presents approaches for self-supervised speech representation learning and their connection to other research areas.
  arXiv  Detail & Related papers  (2022-05-21T16:52:57Z)
• DeepFry: Identifying Vocal Fry Using Deep Neural Networks [16.489251286870704]
  Vocal fry or creaky voice refers to a voice quality characterized by irregular glottal opening and low pitch. Due to its irregular periodicity, creaky voice challenges automatic speech processing and recognition systems. This paper proposes a deep learning model to detect creaky voice in fluent speech.
  arXiv  Detail & Related papers  (2022-03-31T13:23:24Z)
• Self-supervised Learning with Random-projection Quantizer for Speech Recognition [51.24368930992091]
  We present a simple and effective self-supervised learning approach for speech recognition. The approach learns a model to predict masked speech signals, in the form of discrete labels. It achieves similar word-error-rates as previous work using self-supervised learning with non-streaming models.
  arXiv  Detail & Related papers  (2022-02-03T21:29:04Z)
• Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks [20.316239155843963]
  We propose a self-supervised audio representation learning method and apply it to a variety of downstream non-speech audio tasks. On the AudioSet benchmark, we achieve a mean average precision (mAP) score of 0.415, which is a new state-of-the-art on this dataset.
  arXiv  Detail & Related papers  (2021-10-14T12:32:40Z)
• English Accent Accuracy Analysis in a State-of-the-Art Automatic Speech Recognition System [3.4888132404740797]
  We evaluate a state-of-the-art automatic speech recognition model, using unseen data from a corpus with a wide variety of labeled English accents. We show that there is indeed an accuracy bias in terms of accentual variety, favoring the accents most prevalent in the training corpus.
  arXiv  Detail & Related papers  (2021-05-09T08:24:33Z)
• End-to-End Diarization for Variable Number of Speakers with Local-Global Networks and Discriminative Speaker Embeddings [66.50782702086575]
  We present an end-to-end deep network model that performs meeting diarization from single-channel audio recordings. The proposed system is designed to handle meetings with unknown numbers of speakers, using variable-number permutation-invariant cross-entropy based loss functions.
  arXiv  Detail & Related papers  (2021-05-05T14:55:29Z)
• Streaming Multi-talker Speech Recognition with Joint Speaker Identification [77.46617674133556]
  SURIT employs the recurrent neural network transducer (RNN-T) as the backbone for both speech recognition and speaker identification. We validate our idea on the Librispeech dataset -- a multi-talker dataset derived from Librispeech, and present encouraging results.
  arXiv  Detail & Related papers  (2021-04-05T18:37:33Z)
• Unsupervised Cross-lingual Representation Learning for Speech Recognition [63.85924123692923]
  XLSR learns cross-lingual speech representations by pretraining a single model from the raw waveform of speech in multiple languages. We build on wav2vec 2.0 which is trained by solving a contrastive task over masked latent speech representations. Experiments show that cross-lingual pretraining significantly outperforms monolingual pretraining.
  arXiv  Detail & Related papers  (2020-06-24T18:25:05Z)
• Multi-talker ASR for an unknown number of sources: Joint training of source counting, separation and ASR [91.87500543591945]
  We develop an end-to-end multi-talker automatic speech recognition system for an unknown number of active speakers. Our experiments show very promising performance in counting accuracy, source separation and speech recognition. Our system generalizes well to a larger number of speakers than it ever saw during training.
  arXiv  Detail & Related papers  (2020-06-04T11:25:50Z)
• Generative Adversarial Training Data Adaptation for Very Low-resource Automatic Speech Recognition [31.808145263757105]
  We use CycleGAN-based non-parallel voice conversion technology to forge a labeled training data that is close to the test speaker's speech. We evaluate this speaker adaptation approach on two low-resource corpora, namely, Ainu and Mboshi.
  arXiv  Detail & Related papers  (2020-05-19T07:35:14Z)

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.