Nonverbal vocalizations (NVVs), such as laughter and sighs, convey meaning alongside words. NVV-aware speech systems must recognize and generate these events while preserving lexical accuracy and perceptual quality. Yet differences in annotation, resources, and evaluation complicate comparisons, and aggregate scores ca...
Liu-Meng Xue, Yuang Cao, Jing-Bin Hu et al.· 0 citations
We propose a preference optimization framework with Large Audio-Language Model (LALM) feedback for controllable non-verbal vocalization (NVV) generation in continuous autoregressive speech models. To construct preference data without human preference annotation, we build a bilingual prompt corpus by combining NVV-injec...
Jing-Bin Hu, Qi-Rui Zhan, Yuang Cao et al.· 3 citations
Recent controllable speech generation requires training data with fine-grained annotations of speaker traits, prosody, emotion, paralinguistic cues, acoustic scenes, and context. Existing workflows often rely on manual correction, paid hosted multimodal services, or fixed processing chains, which limits large-scale dat...
Qi-Rui Zhan, Shui-Yuan Wang, Jing-Bin Hu et al.· 0 citations
Non-verbal vocalizations (NVVs) carry important paralinguistic information but are often omitted by conventional automatic speech recognition (ASR) systems. The ISCSLP NVVSpeech Challenge requires joint transcription of lexical content and 16 NVV categories under limited and highly imbalanced supervision. We present a...
Shang-Yue Jia, Jing-Ru Ma, Yang-Ze Li et al.· 1 citation
A Context-Aware Direct Preference Optimization (CA-DPO) method that significantly outperforms the baseline across all objective and subjective metrics, and establishes an evaluation method featuring a 500-sample test set and an LLM-as-Judge framework to independently assess reasoning and execution fidelity.
Jing-Bin Hu, Lu-Yu Wang, Wen-Jie Tian et al.· 0 citations
An NVV-aware DiTAR system that models continuous speech latents, encodes the 16 target NVV categories as dedicated to- kens, and adapts stop prediction to distinguish mid-utterance vocalizations from utterance boundaries is developed.
Zi-Yu Zhang, Yun Chen, Tai-Hui Wang et al.· 1 citation· ⚡1
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