StyleBench: Evaluating Speech Language Models on Conversational Speaking Style Control
arXiv:2603.07599v1 Announce Type: new Abstract: Speech language models (SLMs) have significantly extended the interactive capability of text-based Large Language Models (LLMs) by incorporating paralinguistic information. For more realistic interactive experience with customized styles, current SLMs have managed to interpret and control speaking style intensity from user prompts during the dialogue process. However, there remains a lack of systematic benchmarks that quantifies and evaluates the style intensity control ability in conversations. In this paper, we propose StyleBench, a multi-turn dialogue benchmark for comprehensively evaluating the style intensity control ability across four dimensions: emotion, speed, volume, and pitch. Our results reveal the performance gaps between leading SLMs and omni language models (OLMs), suggesting the underlying reasons and promising approaches for future exploration.
arXiv:2603.07599v1 Announce Type: new Abstract: Speech language models (SLMs) have significantly extended the interactive capability of text-based Large Language Models (LLMs) by incorporating paralinguistic information. For more realistic interactive experience with customized styles, current SLMs have managed to interpret and control speaking style intensity from user prompts during the dialogue process. However, there remains a lack of systematic benchmarks that quantifies and evaluates the style intensity control ability in conversations. In this paper, we propose StyleBench, a multi-turn dialogue benchmark for comprehensively evaluating the style intensity control ability across four dimensions: emotion, speed, volume, and pitch. Our results reveal the performance gaps between leading SLMs and omni language models (OLMs), suggesting the underlying reasons and promising approaches for future exploration.
Executive Summary
The article introduces StyleBench, a novel benchmark for evaluating speech language models' ability to control conversational speaking style across four dimensions: emotion, speed, volume, and pitch. The authors highlight the performance gaps between leading speech language models and omni language models, providing insights into the underlying reasons and potential approaches for improvement. This study contributes to the development of more realistic and interactive language models, with implications for various applications, including human-computer interaction and virtual assistants.
Key Points
- ▸ Introduction of StyleBench, a multi-turn dialogue benchmark
- ▸ Evaluation of speech language models' style intensity control ability
- ▸ Performance comparison between speech language models and omni language models
Merits
Comprehensive Evaluation Framework
StyleBench provides a systematic and multi-dimensional approach to assessing speech language models' speaking style control capabilities.
Demerits
Limited Generalizability
The study's findings may not be directly applicable to all types of language models or conversational scenarios, potentially limiting the benchmark's generalizability.
Expert Commentary
The introduction of StyleBench marks a significant step forward in the evaluation of speech language models' speaking style control capabilities. The benchmark's multi-dimensional approach and comprehensive framework provide a nuanced understanding of these models' strengths and limitations. However, further research is needed to address the potential limitations and generalizability of the benchmark. As the field continues to evolve, it is essential to consider the broader implications of advanced language models, including their potential impact on human-computer interaction and the need for regulatory frameworks to ensure responsible development and deployment.
Recommendations
- ✓ Future studies should investigate the application of StyleBench to diverse conversational scenarios and language models
- ✓ Researchers should explore the development of more advanced and generalizable benchmarks for evaluating speech language models' speaking style control capabilities