Utilizing Self-supervised Representations for MOS Prediction

• URL: http://arxiv.org/abs/2104.03017v1
• Date: Wed, 7 Apr 2021 09:44:36 GMT
• Title: Utilizing Self-supervised Representations for MOS Prediction
• Authors: Wei-Cheng Tseng, Chien-yu Huang, Wei-Tsung Kao, Yist Y. Lin, Hung-yi Lee
• Abstract summary: Existing evaluations usually require clean references or parallel ground truth data. Subjective tests, on the other hand, do not need any additional clean or parallel data and correlates better to human perception. We develop an automatic evaluation approach that correlates well with human perception while not requiring ground truth data.
• Score: 51.09985767946843
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Speech quality assessment has been a critical issue in speech processing for decades. Existing automatic evaluations usually require clean references or parallel ground truth data, which is infeasible when the amount of data soars. Subjective tests, on the other hand, do not need any additional clean or parallel data and correlates better to human perception. However, such a test is expensive and time-consuming because crowd work is necessary. It thus becomes highly desired to develop an automatic evaluation approach that correlates well with human perception while not requiring ground truth data. In this paper, we use self-supervised pre-trained models for MOS prediction. We show their representations can distinguish between clean and noisy audios. Then, we fine-tune these pre-trained models followed by simple linear layers in an end-to-end manner. The experiment results showed that our framework outperforms the two previous state-of-the-art models by a significant improvement on Voice Conversion Challenge 2018 and achieves comparable or superior performance on Voice Conversion Challenge 2016. We also conducted an ablation study to further investigate how each module benefits the task. The experiment results are implemented and reproducible with publicly available toolkits.

Related papers

• Beyond human subjectivity and error: a novel AI grading system [67.410870290301]
  The grading of open-ended questions is a high-effort, high-impact task in education. Recent breakthroughs in AI technology might facilitate such automation, but this has not been demonstrated at scale. We introduce a novel automatic short answer grading (ASAG) system.
  arXiv  Detail & Related papers  (2024-05-07T13:49:59Z)
• Understanding and Mitigating the Label Noise in Pre-training on Downstream Tasks [91.15120211190519]
  This paper aims to understand the nature of noise in pre-training datasets and to mitigate its impact on downstream tasks. We propose a light-weight black-box tuning method (NMTune) to affine the feature space to mitigate the malignant effect of noise.
  arXiv  Detail & Related papers  (2023-09-29T06:18:15Z)
• 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)
• Analyzing Robustness of End-to-End Neural Models for Automatic Speech Recognition [11.489161072526677]
  We investigate robustness properties of pre-trained neural models for automatic speech recognition. In this work, we perform a robustness analysis of the pre-trained neural models wav2vec2, HuBERT and DistilHuBERT on the LibriSpeech and TIMIT datasets.
  arXiv  Detail & Related papers  (2022-08-17T20:00:54Z)
• Reassessing Evaluation Practices in Visual Question Answering: A Case Study on Out-of-Distribution Generalization [27.437077941786768]
  Vision-and-language (V&L) models pretrained on large-scale multimodal data have demonstrated strong performance on various tasks. We evaluate two pretrained V&L models under different settings by conducting cross-dataset evaluations. We find that these models tend to learn to solve the benchmark, rather than learning the high-level skills required by the VQA task.
  arXiv  Detail & Related papers  (2022-05-24T16:44:45Z)
• Improving Self-Supervised Learning-based MOS Prediction Networks [0.0]
  The present work introduces data-, training- and post-training specific improvements to a previous self-supervised learning-based MOS prediction model. We used a wav2vec 2.0 model pre-trained on LibriSpeech, extended with LSTM and non-linear dense layers. The methods are evaluated using the shared synthetic speech dataset of the first Voice MOS challenge.
  arXiv  Detail & Related papers  (2022-04-23T09:19:16Z)
• A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems [25.468558523679363]
  We train and compare gender-specific wav2vec 2.0 models against models containing different degrees of gender balance in pre-training data. We observe lower overall performance using gender-specific pre-training before fine-tuning an end-to-end ASR system.
  arXiv  Detail & Related papers  (2022-04-04T11:28:19Z)
• Impact of Pretraining Term Frequencies on Few-Shot Reasoning [51.990349528930125]
  We investigate how well pretrained language models reason with terms that are less frequent in the pretraining data. We measure the strength of this correlation for a number of GPT-based language models on various numerical deduction tasks. Although LMs exhibit strong performance at few-shot numerical reasoning tasks, our results raise the question of how much models actually generalize beyond pretraining data.
  arXiv  Detail & Related papers  (2022-02-15T05:43:54Z)
• LDNet: Unified Listener Dependent Modeling in MOS Prediction for Synthetic Speech [67.88748572167309]
  We present LDNet, a unified framework for mean opinion score (MOS) prediction. We propose two inference methods that provide more stable results and efficient computation.
  arXiv  Detail & Related papers  (2021-10-18T08:52:31Z)

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.