Dual-Form ASR (DF-ASR) is a framework that unifies spoken-form ASR and semantics-aware written-form inverse text normalization (ITN) for Chinese speech recognition. It uses paired spoken- and written-form supervision generated and judged by a large language model, and introduces an ITN-MWER objective to penalize errors on normalization-sensitive spans. DF-ASR also employs a REQUIRE-ITN/FORBID-ITN protocol to separately evaluate required normalization and forbidden-span preservation, achieving superior performance over open-source ASR-ITN systems while maintaining prompt-level control between transcript forms.
By Fengrun Zhang, Li Fu, Wangjin Zhou, Lu Fan, Youzheng Wu, Xiaodong He
Uni-Stream is a unified streaming speech language model that supports both speech recognition and synthesis within a single architecture. Built on compact pretrained speech models, it processes interleaved text and speech in real time and achieves performance comparable to task‑specific models through a two‑stage training approach. The authors also introduce a data annotation pipeline for streaming training and plan to release the generated transcriptions and word‑level timestamps, while analyzing how speech and text representations occupy distinct latent spaces in the model.
By Xuan Shi, Chang Zeng, Tiantian Feng, Ye Zhang, Jun Liu, Shih-Heng Wang, Lie Lu, Shrikanth Narayanan
arXiv:2609.14991v1 Announce Type: new
Abstract: Open Thai automatic speech recognition (ASR) is dominated by offline, Whisper-based models that read the whole utterance before transcribing, ruling ou...
By Warit Sirichotedumrong, Tanawin Samutsin, Shah Faisal Wani, Sittipong Sripaisarnmongkol, Kunat Pipatanakul
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
arXiv:2610.09321v1 Announce Type: new
Abstract: Speech Language Model (SLM) performance often degrades on dialects due to data scarcity. Conventional text-to-speech (TTS) augmentation struggles to co...
By Shunsuke Mitsumori, Tomoya Mizumoto, Yusuke Fujita
Open Thai automatic speech recognition (ASR) is dominated by offline, Whisper-based models that read the whole utterance before transcribing, ruling out low-latency uses such as live captioning and vo...
Speech Language Model (SLM) performance often degrades on dialects due to data scarcity. Conventional text-to-speech (TTS) augmentation struggles to cover diverse dialects as it requires a certain amo...
Vietnamese pre-trained encoders have improved rapidly, but the strongest ones are large, multilingual-vocabulary models or depend on external word segmentation, which makes them costly to deploy. We p...
The paper investigates the role of intermediate neural networks in end‑to‑end speech parsing, a task that jointly predicts transcription and syntactic structure. By removing these intermediate units, the authors reduce model parameters by 12% while matching or surpassing prior performance on ASR and parsing across French, Slovenian, and Naija. They find that intermediate layers mainly help close the representational gap when the pretrained encoder is frozen and analyze how training data size and encoder depth affect parsing performance.
By Minnie Kabra, Benjamin Lecouteux, Maximin Coavoux
The paper introduces two new policies—Recent Frame Attention Policy (RFAP) and Dual-Condition Attention Policy (DCAP)—for simultaneous speech-to-text translation. These policies leverage the cross‑attention mechanism of encoder‑decoder models to determine optimal moments for partial translation, enabling offline models to operate in streaming scenarios without extra training. Experiments on the CVSS‑C corpus show RFAP improves BLEU scores by up to 4.0 points while cutting delay by nearly one second, and DCAP maintains high quality at very low latency.
By Filip T\u{a}\c{s}\u{a}dan, Ema Tomanov\'a, Ondrej Lopuch, Pawe{\l} Bilko, Anders S{\o}gaard
arXiv:2603. 05299v2 Announce Type: replace-cross Abstract: Large language models show that simple autoregressive training can yield scalable and coherent generation, but extending this paradigm to speech remains challenging due to the entanglement of semantic and acoustic information.
By Luca Della Libera, Cem Subakan, Mirco Ravanelli
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...
By Sangmin Lee, Yejin Jeon, David Ifeoluwa Adelani