Speech-Hands: A Self-Reflection Voice Agentic Approach to Speech Recognition and Audio Reasoning with Omni Perception

• URL: http://arxiv.org/abs/2601.09413v1
• Date: Wed, 14 Jan 2026 12:06:50 GMT
• Title: Speech-Hands: A Self-Reflection Voice Agentic Approach to Speech Recognition and Audio Reasoning with Omni Perception
• Authors: Zhen Wan, Chao-Han Huck Yang, Jinchuan Tian, Hanrong Ye, Ankita Pasad, Szu-wei Fu, Arushi Goel, Ryo Hachiuma, Shizhe Diao, Kunal Dhawan, Sreyan Ghosh, Yusuke Hirota, Zhehuai Chen, Rafael Valle, Ehsan Hosseini Asl, Chenhui Chu, Shinji Watanabe, Yu-Chiang Frank Wang, Boris Ginsburg,
• Abstract summary: 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.<n>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.<n>To address this, our framework, Speech-Hands, recasts the problem as an explicit self-reflection decision. This learnable primitive proves effective in preventing the model from being derailed by flawed external candidates.
• Score: 142.4692205981783
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• SpeakerSleuth: Evaluating Large Audio-Language Models as Judges for Multi-turn Speaker Consistency [12.420484491347073]
  We present SpeakerSleuth, a benchmark evaluating whether LALMs can reliably judge speaker consistency in multi-turn dialogues.<n>We construct 1,818 human-verified evaluation instances across four diverse datasets spanning synthetic and real speech.<n>We find that models struggle to reliably detect acoustic inconsistencies.
  arXiv  Detail & Related papers  (2026-01-07T15:45:41Z)
• Step-Audio-R1 Technical Report [70.37077572409319]
  We introduce Step-Audio-R1, the first audio reasoning model that successfully unlocks reasoning capabilities in the audio domain.<n>Our model exhibits strong audio reasoning capabilities, surpassing Gemini 2.5 Pro and achieving performance comparable to the state-of-the-art Gemini 3 Pro.
  arXiv  Detail & Related papers  (2025-11-19T20:12:50Z)
• Lost in Phonation: Voice Quality Variation as an Evaluation Dimension for Speech Foundation Models [22.710371114925763]
  Speech foundation models (SFMs) have enabled the direct processing of spoken language from raw audio, bypassing intermediate textual representations.<n>This capability allows SFMs to be exposed to, and potentially respond to, rich paralinguistic variations embedded in the input speech signal.<n>We introduce a new parallel dataset featuring synthesized modifications to voice quality, designed to evaluate SFM responses to creaky and breathy voice.
  arXiv  Detail & Related papers  (2025-10-29T14:44:44Z)
• DELULU: Discriminative Embedding Learning Using Latent Units for Speaker-Aware Self-Supervised Speech Foundational Model [65.93900011975238]
  DELULU is a speaker-aware self-supervised foundational model for verification, diarization, and profiling applications.<n>It is trained using a dual objective that combines masked prediction and denoising, further enhancing robustness and generalization.<n>Our findings demonstrate that DELULU is a strong universal encoder for speaker-aware speech processing, enabling superior performance even without task-specific fine-tuning.
  arXiv  Detail & Related papers  (2025-10-20T15:35:55Z)
• AudioJudge: Understanding What Works in Large Audio Model Based Speech Evaluation [55.607230723223346]
  This work presents a systematic study of Large Audio Model (LAM) as a Judge, AudioJudge, investigating whether it can provide a unified evaluation framework that addresses both challenges.<n>We explore AudioJudge across audio characteristic detection tasks, including pronunciation, speaking rate, speaker identification and speech quality, and system-level human preference simulation for automated benchmarking.<n>We introduce a multi-aspect ensemble AudioJudge to enable general-purpose multi-aspect audio evaluation. This method decomposes speech assessment into specialized judges for lexical content, speech quality, and paralinguistic features, achieving up to 0.91 Spearman correlation with human preferences on
  arXiv  Detail & Related papers  (2025-07-17T00:39:18Z)
• Conformer-Based Self-Supervised Learning for Non-Speech Audio Tasks [20.316239155843963]
  We propose a self-supervised audio representation learning method and apply it to a variety of downstream non-speech audio tasks. On the AudioSet benchmark, we achieve a mean average precision (mAP) score of 0.415, which is a new state-of-the-art on this dataset.
  arXiv  Detail & Related papers  (2021-10-14T12:32:40Z)
• Does Visual Self-Supervision Improve Learning of Speech Representations for Emotion Recognition? [63.564385139097624]
  This work investigates visual self-supervision via face reconstruction to guide the learning of audio representations. We show that a multi-task combination of the proposed visual and audio self-supervision is beneficial for learning richer features. We evaluate our learned audio representations for discrete emotion recognition, continuous affect recognition and automatic speech recognition.
  arXiv  Detail & Related papers  (2020-05-04T11:33:40Z)
• Speech Enhancement using Self-Adaptation and Multi-Head Self-Attention [70.82604384963679]
  This paper investigates a self-adaptation method for speech enhancement using auxiliary speaker-aware features. We extract a speaker representation used for adaptation directly from the test utterance.
  arXiv  Detail & Related papers  (2020-02-14T05:05:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.