Robust Accent Identification via Voice Conversion and Non-Timbral Embeddings

Automatic accent identification (AID) remains a challenging task due to the complex variability of accents, the entanglement of accent cues with speaker traits, and the scarcity of reliable accentlabelled data. To address these challenges, we propose a speaker augmentation strategy using voice conversion (VC), with which we generate additional training data by converting original training utterances into different speaker voices while preserving accentual cues. For this purpose, we select two recent VC systems and evaluate their capability to preserve accent. Alternatively, we also explore the use of non-timbral embeddings in AID, for their ability to convey accent information among other non timbral cues. The effectiveness of both methods is demonstrated on the GenAID benchmark, achieving a new state-of-the-art F1-score of 0.66, compared to the previous score of 0.55. Beyond AID, we show that non-timbral embeddings enable accent-controlled Text-to-Speech, producing high-fidelity speech with accurate accent transfer.

Identity leakage through accent cues in voice anonymisation

Voice anonymisation is used to conceal voice identity while preserving linguistic content. Even if anonymisation seems strong, non-timbral cues such as accent that remain post-anonymisation can help re-identification and reveal sensitive socio-demographic traits. We report a study of residual accent information involving multiple anonymisation systems. We highlight the role of accent using speaker verification, accent verification, and accent classification using a set of embeddings focusing on timbral, non-timbral and accent-related information and show the extent to which related cues facilitate reidentification post anonymisation. Results show that, while some systems are robust to reidentification attempts using accent cues, others leave residual, speaker-dependent, accentrelated cues which can be used to reveal the voice identity. We also highlight accent-dependent variation in anonymisation performance, raising fairness concerns, and show that a system with characterlevel conditioning can help obfuscate identity-revealing accent cues, reducing accent-identification accuracy by 68% on average and improving overall anonymisation performance by 11% relative.