TellWhisper: Tell Whisper Who Speaks When
Abstract
Multi-speaker automatic speech recognition (MASR) aims to predict ''who spoke when and what'' from multi-speaker speech, a key technology for multi-party dialogue understanding. However, most existing approaches decouple temporal modeling and speaker modeling when addressing ''when'' and ''who'': some inject speaker cues before encoding (e.g., speaker masking), which can cause irreversible information loss; others fuse identity by mixing speaker posteriors after encoding, which may entangle acoustic content with speaker identity. This separation is brittle under rapid turn-taking and overlapping speech, often leading to degraded performance. To address these limitations, we propose TellWhisper, a unified framework that jointly models speaker identity and temporal within the speech encoder. Specifically, we design TS-RoPE, a time-speaker rotary positional encoding: time coordinates are derived from frame indices, while speaker coordinates are derived from speaker activity and pause cues. By applying region-specific rotation angles, the model explicitly captures per-speaker continuity, speaker-turn transitions, and state dynamics, enabling the attention mechanism to simultaneously attend to ''when'' and ''who''. Moreover, to estimate frame-level speaker activity, we develop Hyper-SD, which casts speaker classification in hyperbolic space to enhance inter-class separation and refine speaker-activity estimates. Extensive experiments demonstrate the effectiveness of the proposed approach.
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 TellWhisper, a unified framework for multi-speaker automatic speech recognition that effectively integrates temporal and speaker dynamics. This innovative approach has the potential to significantly advance the field of speech recognition, particularly in complex multi-speaker environments.
Methodology
The methodology presented in this paper is innovative, particularly with the introduction of the TS-RoPE (time-speaker rotary positional encoding) that integrates temporal and speaker information directly into the speech encoder. This approach addresses the common pitfalls of existing MASR systems that treat speaker and temporal modeling separately, which often leads to performance degradation in overlapping speech scenarios. The use of hyperbolic space for speaker classification through Hyper-SD is also a novel contribution that enhances inter-class separation, thus providing a robust framework for speaker activity estimation. The overall architecture is well-structured, with clear delineation of components and their interactions.
Results & evaluation
The experiments conducted are extensive and well-designed, utilizing multiple datasets that reflect real-world conditions. The evaluation metrics are appropriate for the task, particularly given the complexities of speaker attribution and temporal alignment in MASR. The results indicate significant improvements over baseline models, showcasing the effectiveness of the proposed methods. The ablation studies provide valuable insights into the contribution of each component, reinforcing the robustness of the findings.
Reproducibility
While the paper provides a detailed description of the methodology and experimental setup, the lack of a publicly available code repository or demo limits reproducibility. Clear hyperparameter settings and training strategies are discussed, but without access to the implementation, it would be challenging for other researchers to replicate the results.
Limitations
One limitation is the absence of a demo or project URL, which would facilitate further exploration of the proposed methods. Additionally, while the paper demonstrates improvements over existing methods, it does not fully address potential scalability issues or performance in highly diverse real-world environments. The reliance on hyperbolic space may also introduce complexity in understanding and interpreting the model's behavior.
Broader impact
The implications of this research are significant for applications in multi-party dialogue systems, enhancing the capabilities of automatic transcription services, virtual assistants, and collaborative tools. Improved MASR can lead to better accessibility and usability in various domains, including education, business, and healthcare. The paper presents TellWhisper, a unified framework for multi-speaker automatic speech recognition that effectively integrates temporal and speaker dynamics. This innovative approach has the potential to significantly advance the field of speech recognition, particularly in complex multi-speaker environments.
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