When reinforcement learning (RL) is used for post-training automatic speech recognition (ASR), the reward almost always lives in the text space: it compares a hypothesis with the reference and never checks whether the hypothesis is supported by the audio. On highly regular speech this licenses a shortcut - guessing fro...
Ting-Zhen Xiong, Rilin Chen, Wei-Wei Li et al.· 0 citations
Controllable synthesis of nonverbal vocalizations (NVVs) is es- sential for natural and expressive speech, but remains challeng- ing due to their acoustic diversity and imbalanced distribution in existing corpora. To address these challenges, we develop an NVV-aware DiTAR system that models continuous speech latents, e...
Zi-Yu Zhang, Yun Chen, Tai-Hui Wang et al.· 0 citations
AuK, an open-source foundational model that unifies speech generation and editing through a common interface of natural-language instructions and audio context, is introduced and leading performance on zero-shot and instruction-controlled speech generation and general instruction-guided editing is demonstrated.
Zi-Yang Ma, Zhi-Kang Niu, Wen-Ming Tu et al.· 2 citations· ⚡1
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