VStyle: A Benchmark for Voice Style Adaptation with Spoken Instructions
Spoken language models (SLMs) have emerged as a unified paradigm for speech
understanding and generation, enabling natural human machine interaction.
However, while most progress has focused on semantic accuracy and instruction
following, the ability of SLMs to adapt their speaking style based on spoken
instructions has received limited attention. We introduce Voice Style
Adaptation (VSA), a new task that examines whether SLMs can modify their
speaking style, such as timbre, prosody, or persona following natural language
spoken commands. To study this task, we present VStyle, a bilingual (Chinese &
English) benchmark covering four categories of speech generation: acoustic
attributes, natural language instruction, role play, and implicit empathy. We
also introduce the Large Audio Language Model as a Judge (LALM as a Judge)
framework, which progressively evaluates outputs along textual faithfulness,
style adherence, and naturalness, ensuring reproducible and objective
assessment. Experiments on commercial systems and open source SLMs demonstrate
that current models face clear limitations in controllable style adaptation,
highlighting both the novelty and challenge of this task. By releasing VStyle
and its evaluation toolkit, we aim to provide the community with a foundation
for advancing human centered spoken interaction. The dataset and code are
publicly available at
https://junzhan2000.github.io/VStyle.github.io/{project's homepage}.
68c76d28ee0eed1697d6b67a
Jun Zhan
68c76d28ee0eed1697d6b67b
Mingyang Han
68c76d28ee0eed1697d6b67c
Yuxuan Xie
68c76d28ee0eed1697d6b67d
Chen Wang
68c76d28ee0eed1697d6b67e
Dong Zhang
68c76d28ee0eed1697d6b67f
Kexin Huang
68c76d28ee0eed1697d6b680
Haoxiang Shi
68c76d28ee0eed1697d6b681
DongXiao Wang
68c76d28ee0eed1697d6b682
Tengtao Song
68c76d28ee0eed1697d6b683
Qinyuan Cheng
68c76d28ee0eed1697d6b684
Shimin Li
68c76d28ee0eed1697d6b685
Jun Song
68c76d28ee0eed1697d6b686
Xipeng Qiu
68c76d28ee0eed1697d6b687
Bo Zheng
VStyle: A Benchmark for Voice Style Adaptation with Spoken Instructions
Spoken language models (SLMs) have emerged as a unified paradigm for speech
understanding and generation, enabling natural human machine interaction.
However, while most progress has focused on semantic accuracy and instruction
following, the ability of SLMs to adapt their speaking style based on spoken
instructions has received limited attention. We introduce Voice Style
Adaptation (VSA), a new task that examines whether SLMs can modify their
speaking style, such as timbre, prosody, or persona following natural language
spoken commands. To study this task, we present VStyle, a bilingual (Chinese &
English) benchmark covering four categories of speech generation: acoustic
attributes, natural language instruction, role play, and implicit empathy. We
also introduce the Large Audio Language Model as a Judge (LALM as a Judge)
framework, which progressively evaluates outputs along textual faithfulness,
style adherence, and naturalness, ensuring reproducible and objective
assessment. Experiments on commercial systems and open source SLMs demonstrate
that current models face clear limitations in controllable style adaptation,
highlighting both the novelty and challenge of this task. By releasing VStyle
and its evaluation toolkit, we aim to provide the community with a foundation
for advancing human centered spoken interaction. The dataset and code are
publicly available at
https://junzhan2000.github.io/VStyle.github.io/{project's homepage}.
Voice Style Adaptation (VSA) evaluates the ability of spoken language models to modify their speaking style based on spoken instructions, using a bilingual benchmark and a Large Audio Language Model as a Judge framework.
spoken language models Voice Style Adaptation VStyle acoustic attributes natural language instruction role play implicit empathy Large Audio Language Model as a Judge textual faithfulness style adherence naturalness