基于激光多普勒测振仪技术的自动语音识别系统的数据增强方法。

IF 1.4 Q3 ACOUSTICS
Ji-Yan Han, Po-Hsun Huang, Ruei-Ci Shen, Cheng-Yang Liu, Lieber Po-Hung Li, An-Suey Shiao, Ying-Hui Lai
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引用次数: 0

摘要

在低信噪比和远距离语音等具有挑战性的条件下,基于麦克风的自动语音识别(ASR)在清晰度方面存在问题。为了解决这个问题,将激光多普勒振动仪(LDV)技术集成到ASR系统中,并采用数据增强方法生成包含LDV属性的训练数据。使用单词错误率评估ASR的性能,与仅使用真实LDV数据训练的基线ASR系统相比,数据增强方法显示出更好的结果。因此,在数据增强的帮助下,LDV有可能成为ASR的声音捕捉设备,为未来的应用提供有价值的见解。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A data augmentation approach for an automatic speech recognition system using laser Doppler vibrometer technology.

In challenging conditions such as low signal-to-noise ratios and distant speech, microphone-based automatic speech recognition (ASR) struggles with clarity. To remedy this, laser Doppler vibrometer (LDV) technology is integrated into the ASR system and a data augmentation approach is employed to generate training data containing LDV attributes. The performance of the ASR, assessed using word error rates, showed superior results with the data augmentation approach compared to the baseline ASR system trained solely on real LDV data. Thus, with the aid of data augmentation, LDV can potentially be a sound-capturing device for ASR, offering valuable insights for future applications.

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