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.
arXiv:2608. 04586v1 Announce Type: cross Abstract: Multimodal large language models (MLLMs) have achieved significant success in speech-to-text translation (S2TT).
arXiv:2608. 04586v2 Announce Type: replace-cross Abstract: Multimodal large language models (MLLMs) have achieved significant success in speech-to-text translation (S2TT).
The paper introduces a new strategy for connecting large language models (LLMs) to speech encoders in automatic speech recognition (ASR) systems by sharing a single connector across languages within the same linguistic family. This approach reduces the number of parameters needed compared to training a separate connector for each language, while improving generalization across different domains and real‑world corpora. Experiments with two multilingual LLMs and two speech datasets demonstrate that family‑based connectors are both efficient and effective for multilingual ASR deployment.
arXiv:2606. 03957v1 Announce Type: cross Abstract: Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data.
Conversational ASR for lower-resource languages and niche domains is limited by the scarcity of domain-matched multi-speaker training data. We propose an augmentation pipeline that generates scenario-level dialogues with participant metadata, maps speaker attributes to TTS voice profiles, and assembles synthesized utterances into speaker-aware simulated conversations.
arXiv:2608.29239v1 Announce Type: new Abstract: Low-resource ASR remains difficult because scarce transcripts provide limited supervised evidence for target-side generation. To address this gap, we p...
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 presents a data‑centric approach to improve automatic speech recognition for non‑verbal vocalizations (NVVs) in the ISCSLP NVVSpeech Challenge. It introduces cross‑dataset label harmonization and a two‑stage sampling schedule—first square‑root category sampling to address long‑tailed distributions, then uniform‑category fine‑tuning—to jointly transcribe lexical content and 16 NVV categories. The final system achieved an official score of 63.86, ranking fourth in Track 1.
arXiv:2609.23462v1 Announce Type: cross Abstract: Non-verbal vocalizations (NVVs) carry important paralinguistic information but are often omitted by conventional automatic speech recognition (ASR) s...
arXiv:2603. 29042v2 Announce Type: replace-cross Abstract: Phone recognition (PR) is a key enabler of multilingual and low-resource speech processing tasks, yet robust performance remains elusive.
Nord-Parl-TTS is an open text‑to‑speech dataset for Finnish and Swedish created from recordings of Nordic parliamentary proceedings. It contains 900 hours of Finnish and 5 090 hours of Swedish speech, processed with an adapted Emilia pipeline and accompanied by unified evaluation sets for model development and benchmarking. The dataset aims to reduce the resource gap in TTS between high‑ and lower‑resourced languages.