The paper introduces a token‑level extension of Omni‑Temporal Classification (OTC) for automatic speech recognition, allowing unsupported tokens to be bypassed while preserving supervision for the rest of the word. Across 19 languages and three corpora, this token‑level OTC consistently outperforms standard CTC, achieving the lowest mean word error rate on every dataset and a 9.45% average relative WER reduction. A predictive‑entropy‑indexed schedule replaces epoch‑based relaxation, reducing training‑length dependence while maintaining performance.
By Saurabh Kumar, Diptiman Mohanta, Prasanta Kumar Ghosh
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.
Current speech translation systems, including SpeechLLMs, are trained on cleaned text and tend to strip disfluencies like filled pauses and false starts rather than translate them. We show this comes at a cost: disfluencies carry meaning that gets lost when speech is cleaned up.
arXiv:2609.14231v1 Announce Type: cross
Abstract: Controllable synthesis of nonverbal vocalizations (NVVs) is es- sential for natural and expressive speech, but remains challeng- ing due to their aco...
By Ziyu Zhang, Yun Chen, Taihui Wang, Hanzhao Li, Qicong Xie, Rilin Chen, Zhixian Zhao, Lei Xie
arXiv:2609.21663v1 Announce Type: new
Abstract: Word Error Rate (WER), the most commonly used metric for Automatic Speech Recognition (ASR), treats every lexical deviation from the reference as equal...
By Hritika Sharma, Thibault Ba\~neras-Roux, Alessandra Pinto, Petr Motlicek, Hyunggu Jung, Esa\'u Villatoro-Tello, Somang Nam
SPAR-K is a scheduled periodic alternating early‑exit framework for interleaved spoken language models that reduces decoding depth for speech tokens while maintaining quality. It lets most speech positions exit at a fixed intermediate layer and inserts periodic full‑depth refresh steps to counter distribution shift. Experiments on Step‑Audio‑2‑mini and GLM‑4‑Voice show up to 11 % depth reduction with less than 0.82 % drop in question‑answering accuracy and negligible impact on MOS and WER.
By Hsiao-Ying Huang, Cheng-Han Chiang, Hung-yi Lee
arXiv:2609.38658v1 Announce Type: cross
Abstract: TTS systems with autoregressive semantic modeling have demonstrated strong zero-shot voice cloning performance and rich expressive variation, but the...
By Jian Chen, You Zhang, Mark Vinton
The paper introduces Hikari, a policy‑free, end‑to‑end model that performs simultaneous speech‑to‑text translation and streaming transcription. It employs a Decoder Time Dilation mechanism to mitigate overuse of WAIT tokens during training and a supervised fine‑tuning strategy that helps the model recover from delays, improving the quality‑latency trade‑off. Despite its modest size, Hikari achieves competitive translation quality at consistently low latency, outperforming larger published IWSLT 2026 submissions and proprietary API systems on en‑ja, en‑de, and en‑ru tasks.
By Roman Koshkin, Jeon Haesung, Lianbo Liu, Hao Shi, Mengjie Zhao, Yusuke Fujita, Yui Sudo
The paper introduces a three‑stage pipeline to improve accented conversational ASR for speakers from India, Indonesia, and Latin America. It uses heuristic SQL filters to curate entity‑rich training data, regional LoRA adapters fine‑tuned on Qwen2.5‑Omni‑3B to generate both verbatim and corrected transcripts, and a six‑category error taxonomy validated by an LLM judge. The approach raises entity recall to 80‑85% and filler recall to 76‑86%, while keeping WER low (6‑10%) and outperforming Whisper and a commercial ASR on entity recall.
By Fiza Husain, Ankit Pandey, Yash Singh
Although Speech Large Language Models (SpeechLLMs) excel at speech understanding and generation, their capacity for fine-grained, temporally aligned outputs remains underexplored. Our work addresses t...
Modern large language models are pretrained on massive datasets, making it difficult to prevent benchmark data from entering their training sets and undermining the reliability of evaluation results....
arXiv:2605.28456v2 Announce Type: replace
Abstract: Existing Visual Speech Recognition (VSR) systems commonly rely on left-to-right autoregressive decoding, which can force premature decisions on vis...
By Jeong Hun Yeo, Chae Won Kim, Hyeongseop Rha, Yong Man Ro