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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.