arXiv:2608.22196v1 Announce Type: cross Abstract: While cascaded multi-talker ASR (MT-ASR) leverages state-of-the-art foundation models, its performance is often capped by speaker leakage during sepa...
arXiv:2610.24809v2 Announce Type: replace Abstract: Speech recognition in real time in far-field conversational settings still incurs significant errors. Identifying potential error spans during stre...
Speech transcripts used as long-term memory must preserve both words and stable speaker identities. Existing meeting-transcription metrics either ignore speakers or remap anonymous speakers independen...
arXiv:2607. 21820v1 Announce Type: cross Abstract: Audio deepfake detectors are trained to distinguish genuine speech from synthetic speech and often perform well on standard benchmarks.
arXiv:2609.39344v1 Announce Type: cross Abstract: Speech transcripts used as long-term memory must preserve both words and stable speaker identities. Existing meeting-transcription metrics either ign...
The paper introduces GUARD, a lightweight speaker identity unlearning framework designed to prevent re-identification in zero-shot text-to-speech systems. GUARD employs a learned speaker gate and speaker-agnostic activation steering on a frozen TTS backbone, optimizing steering vectors through group-relative reward optimization to reduce similarity to forgotten speakers while maintaining intelligibility and naturalness. Experiments on CosyVoice2 show that GUARD significantly lowers forget-speaker similarity and re-identification accuracy while preserving the ability to reproduce retained speakers.
arXiv:2610.20327v1 Announce Type: new Abstract: India's 22 scheduled languages are spoken by over a billion people in conversations that mix languages, often within one turn, overlap freely, and take...
arXiv:2607. 12569v1 Announce Type: cross Abstract: Fake speech detectors are increasingly challenged by the development of new and more accurate generative models.
arXiv:2606. 17416v1 Announce Type: cross Abstract: Multilingual speaker verification remains challenging because language-dependent acoustic variability causes speaker identity to become entangled with linguistic characteristics, degrading generalization across languages.
The paper introduces an LLM-assisted system for correcting speaker attribution errors during meetings. It combines streaming ASR, diarization, and concise LLM-generated summaries to guide users in providing brief corrective feedback, which updates the transcript and adds online speaker enrollments. The approach includes mechanisms to accurately interpret user corrections and a simulation for large-scale evaluation, achieving significant reductions in DER and speaker substitution error on the AMI headset test set, with a pilot usability study highlighting further improvements.
arXiv:2606. 20518v1 Announce Type: new Abstract: Flow-matching text-to-speech systems achieve remarkable zero-shot quality but remain static after deployment: pronunciation errors on out-of-vocabulary proper nouns persist unless the model is retrained.
arXiv:2607. 08111v1 Announce Type: cross Abstract: Training target speaker extraction (TSE) models for real conversational mixtures remains challenging because large-scale training corpora and clean target speech for supervision are unavailable.