Endpoint Anticipation for Low-Latency Spoken Dialogue
Abstract
While low-latency interaction is critical for spoken dialogue, cascaded architectures are often bottlenecked by reactive turn-completion detection. We propose Endpoint Anticipation, shifting from reactive detection to proactive forecasting of end-of-turn signals. Our speech-based model anticipates endpoints upto 2.56 seconds in advance, enabling speculative execution of LLM and TTS pipelines on partial context. We introduce metrics to quantify the trade-off between realized latency reduction and computational redundancy. Evaluation across conversational and task-oriented datasets shows our model consistently outperforms competitive VAP-based baselines. Integration with the Unmute framework demonstrates a 505 ms average latency reduction with a 28.4% increase in speculative computation, effectively masking sequential bottlenecks to enable complex reasoning in real-time speech-to-speech interaction.
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 significant advancement in low-latency spoken dialogue systems through the introduction of endpoint anticipation, which allows for proactive processing of user speech. This innovative approach, combined with a robust evaluation framework, positions the work as a valuable contribution to the field of audio and machine learning.
Methodology
The paper introduces a novel approach to endpoint anticipation in spoken dialogue systems, shifting from reactive to proactive detection of end-of-turn signals. The dual-stream audio representation and the use of independent binary classification tasks for different anticipation horizons are well-structured and innovative. The proposed metrics for evaluating the trade-off between latency reduction and computational redundancy are a significant contribution to the field, allowing for a more nuanced understanding of system performance. The integration with the Unmute framework demonstrates practical applicability, although the paper could benefit from clearer explanations of the model architecture and training procedures.
Results & evaluation
The evaluation is thorough, utilizing two diverse datasets (SpokenWOZ and Switchboard) to assess the model's performance across various anticipation horizons. The results show a consistent improvement over the VAP baseline, with a notable average latency reduction of 505 ms. The introduction of specific metrics like Median Realized Anticipation and Expected Redundant Computation provides valuable insights into the model's efficiency and effectiveness. However, the paper could enhance its experimental rigor by including more comprehensive ablation studies to analyze the impact of different components of the model.
Reproducibility
The authors mention that they will open-source their implementation, which is a positive step towards reproducibility. However, the paper lacks detailed information on hyperparameter tuning, model training specifics, and the exact configurations used in experiments, which could hinder replication efforts by other researchers.
Limitations
One limitation is the reliance on specific datasets, which may not capture the full variability of real-world conversational speech. Additionally, while the model shows promise in structured dialogues, its performance in more spontaneous, open-domain conversations remains uncertain. The trade-off between latency reduction and computational redundancy, while quantified, may still lead to inefficiencies in certain scenarios, especially in longer dialogues.
Broader impact
The proposed framework has significant implications for real-time spoken dialogue systems, particularly in applications requiring low-latency interactions, such as virtual assistants and customer service bots. By enabling speculative execution of downstream processes, the model could enhance user experience in conversational AI, making interactions feel more natural and responsive. The open-source nature of the project may also foster further research and development in this area. The paper presents a significant advancement in low-latency spoken dialogue systems through the introduction of endpoint anticipation, which allows for proactive processing of user speech. This innovative approach, combined with a robust evaluation framework, positions the work as a valuable contribution to the field of audio and machine learning.
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