TurboBias 2.0: Streaming Context-Biasing for Production-Efficient ASR Systems
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arXiv:2604. 19635v2 Announce Type: replace-cross Abstract: While generative models have set new benchmarks for Target Speaker Extraction (TSE), their inherent reliance on global context precludes deployment in real-time applications.
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...
arXiv:2609.36913v1 Announce Type: cross Abstract: Transcribing domain-specific entities and rare proper nouns remains a major challenge in automatic speech recognition (ASR). In this paper, we propos...
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...
The paper introduces a lightweight ASR head that can be added to full‑duplex speech‑to‑speech models, enabling real‑time user transcription without major architectural changes. The method adds only a few parameters and preserves full‑duplex conversational features such as turn‑taking and barge‑in. Experiments show a streaming WER of 10.21% within the duplex framework and 7.73% when trained as a standalone ASR model, matching state‑of‑the‑art performance.
arXiv:2607. 21042v1 Announce Type: new Abstract: Autoregressive text-to-speech models achieve strong naturalness but suffer from slow inference due to sequential token generation, limiting their deployment in production applications that require low latency.