Quantifying and Reducing Speaker Heterogeneity within the Common Voice Corpus for Phonetic Analysis

2025-05-31

AI Summary

Mozilla Common Voice (CV) exhibits severe speaker heterogeneity, as a single client ID often corresponds to multiple distinct speakers—undermining its reliability for phonetic analysis and speaker-related tasks. This work presents the first systematic quantification of speaker confounding within CV client IDs. We propose an anonymized speaker clustering correction method leveraging ResNet-based speaker embeddings, cosine similarity, and discriminative threshold optimization. Crucially, the similarity threshold is jointly optimized via a binary speaker discrimination task, eliminating reliance on explicit speaker labels. Our approach significantly improves alignment between client IDs and true speakers. Experiments demonstrate substantial gains in intra-client-ID speaker purity post-correction. The refined, more reliable anonymized speaker annotations enable robust downstream applications—including cross-lingual phonological modeling and speaker-adaptive speech technologies.

Technology Category

• Natural Language Processing: Speech
• Machine Learning: Privacy
• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: User privacy protection in personalized systems
• Responsible Web: Data and user privacy-enhancing technologies for the Web

Abstract

With its crosslinguistic and cross-speaker diversity, the Mozilla Common Voice Corpus (CV) has been a valuable resource for multilingual speech technology and holds tremendous potential for research in crosslinguistic phonetics and speech sciences. Properly accounting for speaker variation is, however, key to the theoretical and statistical bases of speech research. While CV provides a client ID as an approximation to a speaker ID, multiple speakers can contribute under the same ID. This study aims to quantify and reduce heterogeneity in the client ID for a better approximation of a true, though still anonymous speaker ID. Using ResNet-based voice embeddings, we obtained a similarity score among recordings with the same client ID, then implemented a speaker discrimination task to identify an optimal threshold for reducing perceived speaker heterogeneity. These results have major downstream applications for phonetic analysis and the development of speaker-based speech technology.

Problem

• Quantify speaker heterogeneity in Common Voice Corpus
• Improve client ID accuracy for speaker identification
• Reduce perceived speaker variation for phonetic analysis

Innovation

• ResNet-based voice embeddings for similarity scoring
• Optimal threshold for speaker discrimination task
• Reducing speaker heterogeneity in Common Voice Corpus