微型麦克风阵列移动机器人便携式关键词识别与声源检测系统设计

Muhammad Bagus Andra, T. Usagawa
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引用次数: 0

摘要

定位声源和检测语音的能力已经成为机器人听觉的基本特征,使其能够与人类互动并执行复杂的任务。最近,微型麦克风阵列的发展使机器人听音变得更加容易,并为构建机器人听力系统提供了一个灵活的平台。本研究提出了一种基于ReSpeaker Core v2.0麦克风阵列的关键词定位与声源检测系统。我们利用频域双耳模型(FDBM)检测声源的到达方向(DOA),并利用机器人的移动性进行三角测量来估计声源的距离。利用基于长短期记忆(LSTM)网络的分离模型来完成关键词识别任务。我们评估了DOA和距离的平均准确率以及关键词发现活动的单词错误率(WER)。我们还比较了系统在开场无混响情况下的性能与理想的仿真结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Portable Keyword Spotting and Sound Source Detection System Design on Mobile Robot with Mini Microphone Array
The capability of locating sound source and detecting speech has been an essential feature in the robot auditory that enables it to and interact with human and perform sophisticated tasks. Recent development of mini microphone array has made robot audition much more accessible and offer a flexible platform to build a robot hearing system. This research proposes a keyword spotting and sound source detection system that is built on ReSpeaker Core v2.0 microphone array. We use Frequency Domain Binaural Model (FDBM) to detect the Direction of Arrival (DOA) of the sound source and estimate the distance of the sound source by taking advantage of the robot mobility to perform a triangulation method. Separate model based on Long-Short Term Memory (LSTM) network is utilized to perform the keyword spotting task. We evaluate the average accuracy of the DOA and distance and Word Error Rate (WER) of the keyword spotting activity. We also compare the performance of the system in the open field non reverberant situation with an ideal simulation.
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