Beyond Fresh Starts: Stateful Inference for Streaming ASR in Conversational Voice Agents

2026-08-22

AI Summary

研究针对语音助手中的长沉默和回话现象导致的识别错误，提出两种状态管理策略以保持跨话语上下文，减少开始时的错误。

Abstract

Modern voice-agent systems rely on streaming speech recognition models that operate under stringent latency constraints. This study shows that, due to the limited memory constraints of real-time processing, these systems are adversely impacted by conversational phenomena such as long silences and backchannels. While many agentic pipelines mitigate this by resetting state at each turn, this approach discards vital context and impairs performance at turn onsets. We propose two state-management strategies that preserve cross-utterance context to reduce onset errors. In experiments with two state-of-the-art streaming models on two spoken dialogue benchmarks, our best method yields an average of 15-21% relative WER reduction at utterance onsets.

Problem

• Streaming ASR
• Latency Constraints
• Conversational Phenomena
• State Management
• Context Preservation

Innovation

• state-management strategies
• cross-utterance context
• streaming ASR
• WER reduction