arXiv subjects

Boya Dong

Publications and source records attributed to Boya Dong.

3 recordsLinked to original sources

arXiv2026

JoyAI-Talker: Full-Duplex Speech Interactive Large Model Built for Empathetic Voice Agents

We present JoyAI-Talker, a full-duplex speech dialogue system that delivers robust foundation model capabilities while empowering empathetic interaction and voice agent intelligence. JoyAI-Talker adopts a modular Thinker-Talker architecture and further implements a unified speech-text joint training pipeline to mitigate the common "cognitive degradation" bottleneck, thereby largely preserving the model's core textual reasoning, STEM, and logical capabilities while extending them to speech-based interaction. For expressive speech synthesis, the Talker module employs a text-controllable generation paradigm that enables natural-language instructions to flexibly control vocal attributes and localized paralinguistic events, such as laughter and sighs, supporting more expressive and fine-grained speech responses. To enhance conversational empathy, we introduce the Persona-Adaptive Empathetic Response (PAER) framework. PAER employs a hierarchical cognitive pipeline to extract non-verbal speaker cues, such as gender, age, and emotional state, from raw input audio, incorporate them into the Thinker's CoT reasoning, and generate context-adaptive responses that align semantically appropriate text with fine-grained control over utterance-level expressiveness and localized paralinguistic events, including sighs, speaking rate, and volume. We further integrate Joy-Duplex, a state-driven, plug-and-play full-duplex framework that functions as an efficient gating engine for real-time turn control. Extensive evaluations show that JoyAI-Talker achieves highly competitive performance on foundational T2T and S2T benchmarks. In full-duplex evaluation, the system reaches a high response rate of 0.88 under user interruptions while maintaining an extremely low false-trigger rate under background speech, demonstrating its readiness for fluid and natural speech dialogue.

cs.SD↗

arXiv2025

JoyVoice: Long-Context Conditioning for Anthropomorphic Multi-Speaker Conversational Synthesis

Large speech generation models are evolving from single-speaker, short sentence synthesis to multi-speaker, long conversation geneartion. Current long-form speech generation models are predominately constrained to dyadic, turn-based interactions. To address this, we introduce JoyVoice, a novel anthropomorphic foundation model designed for flexible, boundary-free synthesis of up to eight speakers. Unlike conventional cascaded systems, JoyVoice employs a unified E2E-Transformer-DiT architecture that utilizes autoregressive hidden representations directly for diffusion inputs, enabling holistic end-to-end optimization. We further propose a MM-Tokenizer operating at a low bitrate of 12.5 Hz, which integrates multitask semantic and MMSE losses to effectively model both semantic and acoustic information. Additionally, the model incorporates robust text front-end processing via large-scale data perturbation. Experiments show that JoyVoice achieves state-of-the-art results in multilingual generation (Chinese, English, Japanese, Korean) and zero-shot voice cloning. JoyVoice achieves top-tier results on both the Seed-TTS-Eval Benchmark and multi-speaker long-form conversational voice cloning tasks, demonstrating superior audio quality and generalization. It achieves significant improvements in prosodic continuity for long-form speech, rhythm richness in multi-speaker conversations, paralinguistic naturalness, besides superior intelligibility. We encourage readers to listen to the demo at https://jea-speech.github.io/JoyVoice

cs.SD↗

arXiv2023

Using a virtual reality interview simulator to explore factors influencing people's behavior

Virtual reality interview simulator (VRIS) provides an effective and manageable approach for candidates prone to being very nervous during interviews, yet, the major anxiety-inducing elements remain unknown. During an interview, the anxiety levels, overall experience, and performance of interviewees might be affected by various circumstances. By analyzing electrodermal activity and questionnaire, we investigated the influence of five variables: (I) \textit{Realism}; (II) \textit{Question type}; (III) \textit{Interviewer attitude}; (IV) \textit{Timing}; and (V) \textit{Preparation}. As such, an orthogonal design $L_8(4^1 \times 2^4)$ with eight experiments ($O A_8$ matrix) was implemented, in which 19 college students took part in the experiments. Considering the anxiety, overall experience, and performance of the interviewees, results indicate that \textit{Question type} plays a major role; secondly, \textit{Realism}, \textit{Preparation}, and \textit{Interviewer attitude} all have some degree of influence; lastly, \textit{Timing} have little to no impact. Specifically, professional interview questions elicited a greater degree of anxiety than personal ones among the categories of interview questions. This work contributes to our understanding of anxiety-stimulating factors during job interviews in virtual reality and provides cues for designing future VRIS.

cs.HC↗