arXiv:2610.19388v1 Announce Type: new Abstract: Massively multilingual speech modeling has made remarkable progress in recent years, yet existing training resources and benchmarks still underrepresen...
arXiv:2607. 23808v1 Announce Type: cross Abstract: In this work, we introduce Indic DiarBench, a speaker diarization and ASR benchmark dataset spanning all 22 scheduled languages of India.
The Public Discourse Corpus (PDC) is the first dataset of public‑figure interview speech annotated for affective valence and epistemic modality. It contains 998 videos from 100 speakers across seven professional domains, yielding 186,642 sentences (3.1 million words). A key methodological contribution is Target Speaker Participation (TSP), a five‑category annotation taxonomy with documented inter‑annotator reliability (κ = 0.616), and an audio‑first diarization pipeline that separates target‑speaker turns from interviewer and third‑party speech. The corpus, annotation tools, validation sample, and processing pipeline are released as open source.
MENASpeechBank is a new reference voice bank that provides about 18,000 high‑quality utterances from 124 speakers across multiple MENA countries, covering English, Modern Standard Arabic, and regional Arabic varieties. The dataset is built through a controllable pipeline that creates persona profiles inspired by the World Values Survey, defines a taxonomy of roughly 5,000 conversational scenarios, matches personas to scenarios via semantic similarity, and generates around 417,000 role‑play conversations using an LLM. Synthetic speaker‑conditioned user‑turn audio is produced from reference recordings to maintain speaker diversity, and both synthetic and human‑recorded conversations are evaluated and analyzed for quality.
arXiv:2510.10774v4 Announce Type: replace-cross Abstract: Persian remains substantially underrepresented in open speech-text resources, limiting progress in multi-speaker text-to-speech (TTS), speech...
The paper introduces methods to improve speech recognition for multilingual video transcription using Whisper-based tools, targeting cross‑cultural understanding. It reports an average transcription error rate of 30% across seven languages, which can be lowered to 20% with modest fine‑tuning. The authors also release associated speech and metadata to aid community refinement of these techniques.
Recent advances in zero-shot text-to-speech (TTS) have substantially improved speech quality and voice cloning fidelity. However, many zero-shot TTS systems still depend on audio prompt transcripts at inference time.
The paper introduces a synthetic Bengali speech dataset tailored for telecom customer‑care applications, comprising 10,000 audio‑text pairs (≈26.82 hours) with predefined train, validation, and test splits. The data were generated using OmniVoice voice‑cloning, and include both original and normalized transcripts for ASR/STT use. Automatic intelligibility evaluation with a fine‑tuned Whisper model shows an average WER of 2.54% and CER of 0.59%, indicating strong text‑audio consistency, while the authors note limitations of synthetic speech and STT‑based evaluation.
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.
arXiv:2610.35114v1 Announce Type: new Abstract: Multilingual transformer models are often assumed to generalise across languages, but their effectiveness for coreference resolution in low-resource se...
SonicCaps is a large-scale audio captioning dataset featuring approximately 15 million captions paired with 700,000 audio clips, created using the Qwen3-Omni multimodal language model. The dataset emphasizes diversity by generating around 24 captions per clip through structured prompt engineering and few-shot generation, covering main descriptions, rephrased variants, and semantic tags. Human evaluations rate SonicCaps higher than existing datasets, and training CLAP models on it improves audio retrieval and zero-shot classification across public and commercial benchmarks.
Omnilingual-MsASR is a new benchmark designed to evaluate omni‑modal large language models on long‑form, multi‑speaker automatic speech recognition across six languages, with recordings up to 30 minutes and up to 20 speakers. The benchmark provides parallel audio and video inputs to test cross‑modal consistency and the models’ ability to use complementary cues from both modalities. Evaluations of leading omni‑modal LLMs show significant performance gaps, with many models achieving word error rates above 100%, underscoring challenges in temporal localization, speaker disambiguation, and cross‑modal synergy.