Title: Multi-Channel Differential ASR for Robust Wearer Speech Recognition on Smart Glasses Show Chinese title

Title: 多通道差分ASR用于智能眼镜上的鲁棒佩戴者语音识别

Abstract: With the growing adoption of wearable devices such as smart glasses for AI assistants, wearer speech recognition (WSR) is becoming increasingly critical to next-generation human-computer interfaces. However, in real environments, interference from side-talk speech remains a significant challenge to WSR and may cause accumulated errors for downstream tasks such as natural language processing. In this work, we introduce a novel multi-channel differential automatic speech recognition (ASR) method for robust WSR on smart glasses. The proposed system takes differential inputs from different frontends that complement each other to improve the robustness of WSR, including a beamformer, microphone selection, and a lightweight side-talk detection model. Evaluations on both simulated and real datasets demonstrate that the proposed system outperforms the traditional approach, achieving up to an 18.0% relative reduction in word error rate. Show Chinese abstract

Abstract: 随着可穿戴设备（如智能眼镜）在人工智能助手中的日益普及，佩戴者语音识别（WSR）对于下一代人机接口变得越来越关键。 然而，在真实环境中，来自旁白语音的干扰仍然是WSR的一个重大挑战，并可能对下游任务（如自然语言处理）造成累积误差。 在本工作中，我们引入了一种新颖的多通道差分自动语音识别（ASR）方法，以实现在智能眼镜上的鲁棒WSR。 该系统从互补的不同前端获取差分输入，以提高WSR的鲁棒性，包括波束成形器、麦克风选择和一个轻量级的旁白检测模型。 在模拟和真实数据集上的评估表明，所提出的系统优于传统方法，实现了单词错误率高达18.0%的相对降低。