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Natural-language style descriptions provide an interpretable interface between large language models (LLMs) and controllable text-to-speech (TTS). However, using descriptions as pseudo-labels compresses target acoustics into text, and descriptive fidelity need not imply effective control of a particular synthesizer. We...
Ruby-ASR is presented, which refines the conventional target into a span-bound orthographic--lexical-reading sequence and shows that refining the recognition target can improve lexical-reading recovery without sacrificing readable orthographic transcription.
This work proposes Hikari, a policy-free, end-to-end model for simultaneous speech-to-text translation and streaming transcription, and presents a supervised fine-tuning strategy that trains the model to recover from delays, significantly improving the quality-latency trade-off.
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