More Speaking Or More Speakers?

Abstract

Self-training (ST) and self-supervised learning (SSL) methods have demonstrated strong improvements in automatic speech recognition (ASR). In spite of these advances, to the best of our knowledge, there is no analysis of how the composition of the labelled and unlabelled datasets used in these methods affects the results. In this work we aim to analyse the effect of number of speakers in the training data on a recent SSL algorithm (wav2vec 2.0), and a recent ST algorithm (slimIPL). We perform a systematic analysis on both labeled and unlabeled data by varying the number of speakers while keeping the number of hours fixed and vice versa. Our findings suggest that SSL requires a large amount of unlabeled data to produce high accuracy results, while ST requires a sufficient number of speakers in the labelled data, especially in the low-regime setting. In this manner these two approaches improve supervised learning in different regimes of data composition.

Stats

• citations0
• S2 citations—
• github stars0
• HF likes0
• heat score0.00
• arxiv keyberrebbi2022more

Related papers

• Analyzing The Factors Affecting Usefulness Of Self-supervised Pre-trained Representations For Speech Recognition (2022)0.00
• Why Does Self-supervised Learning For Speech Recognition Benefit Speaker Recognition? (2022)10.74
• Bigssl: Exploring The Frontier Of Large-scale Semi-supervised Learning For Automatic Speech Recognition (2021)15.73
• Fine-tuning Strategies For Faster Inference Using Speech Self-supervised Models: A Comparative Study (2023)8.35
• Deploying Self-supervised Learning In The Wild For Hybrid Automatic Speech Recognition (2022)0.00
• A Large-scale Probing Analysis Of Speaker-specific Attributes In Self-supervised Speech Representations (2025)0.00
• Investigating Self-supervised Learning For Speech Enhancement And Separation (2022)13.44
• Unispeech-sat: Universal Speech Representation Learning With Speaker Aware Pre-training (2021)0.00