Speech-Hands: A Self-Reflection Voice Agentic Approach to Speech Recognition and Audio Reasoning with Omni Perception
Abstract
We introduce a voice-agentic framework that learns one critical omni-understanding skill: knowing when to trust itself versus when to consult external audio perception. Our work is motivated by a crucial yet counterintuitive finding: naively fine-tuning an omni-model on both speech recognition and external sound understanding tasks often degrades performance, as the model can be easily misled by noisy hypotheses. To address this, our framework, Speech-Hands, recasts the problem as an explicit self-reflection decision. This learnable reflection primitive proves effective in preventing the model from being derailed by flawed external candidates. We show that this agentic action mechanism generalizes naturally from speech recognition to complex, multiple-choice audio reasoning. Across the OpenASR leaderboard, Speech-Hands consistently outperforms strong baselines by 12.1% WER on seven benchmarks. The model also achieves 77.37% accuracy and high F1 on audio QA decisions, showing robust generalization and reliability across diverse audio question answering datasets. By unifying perception and decision-making, our work offers a practical path toward more reliable and resilient audio intelligence.
The following analysis is generated by AI and may contain errors. It is separate from the authors’ abstract and is not peer review.
Research summary
The paper presents a novel framework, Speech-Hands, designed to enhance audio processing by incorporating self-reflection mechanisms, significantly improving performance in speech recognition and audio reasoning tasks.
Methodology
The methodology introduces a novel agentic framework, Speech-Hands, which incorporates self-reflection into audio processing tasks. The use of action tokens ( , , ) to guide decision-making is innovative and allows the model to dynamically choose between its own predictions and external inputs. This approach addresses the limitations of traditional models that do not critically evaluate their outputs. The framework is well-structured, with clear definitions of how the action tokens are generated and utilized during both training and inference. However, the reliance on a single external model could limit the framework's robustness in diverse scenarios.
Results & evaluation
The experimental evaluation is thorough, utilizing multiple datasets across speech recognition and audio question answering tasks. The results demonstrate a significant improvement in performance metrics, such as WER and accuracy, compared to strong baselines. The paper provides a comprehensive analysis of the results, including confusion matrices and case studies that illustrate the model's decision-making process. However, the experiments could benefit from a broader range of external models to validate the framework's effectiveness further.
Reproducibility
The paper lacks detailed implementation specifics, such as code availability or data access, which could hinder reproducibility. While the methodology is described in detail, the absence of a public repository or demo limits the ability of other researchers to replicate the results.
Limitations
The main limitations include the imbalance in action token training, particularly the underrepresentation of the token, which could affect the model's performance in scenarios requiring rewrites. Additionally, the framework's reliance on a single external model may not generalize well across different audio contexts. The authors also acknowledge the need for further exploration of multi-external setups and transfer capabilities.
Broader impact
The proposed framework has significant implications for improving audio intelligence systems, particularly in applications requiring reliable speech recognition and audio reasoning. By enabling models to make informed decisions about when to trust their own outputs versus external inputs, this work could enhance the robustness and reliability of AI systems in real-world audio processing tasks. The approach could be extended to various domains, including assistive technologies, automated transcription services, and interactive voice agents. The paper presents a novel framework, Speech-Hands, designed to enhance audio processing by incorporating self-reflection mechanisms, significantly improving performance in speech recognition and audio reasoning tasks.
Author of this paper? Send a correction.