噪声中第二语言语音的自动识别。

IF 1.2 Q3 ACOUSTICS
Seung-Eun Kim, Bronya R Chernyak, Olga Seleznova, Joseph Keshet, Matthew Goldrick, Ann R Bradlow
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引用次数: 0

摘要

测量人类听者在不同环境条件下识别语音的能力(语音清晰度)是语音通信理论、技术和临床方法面临的一项挑战。目前的黄金标准--人工转录--耗费大量时间和资源。自动语音识别(ASR)系统的最新进展为自动测量可懂度提供了可能。这项研究用第二语言噪音语音测试了 4 种最先进的 ASR 系统,发现其中一种系统(whisper)的准确度达到或超过了人类听者的准确度。然而,whisper 的反应内容与人类的反应有很大差异,尤其是在信噪比较低的情况下,这表明基于 ASR 的语音可懂度建模既有机会也有局限性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic recognition of second language speech-in-noise.

Measuring how well human listeners recognize speech under varying environmental conditions (speech intelligibility) is a challenge for theoretical, technological, and clinical approaches to speech communication. The current gold standard-human transcription-is time- and resource-intensive. Recent advances in automatic speech recognition (ASR) systems raise the possibility of automating intelligibility measurement. This study tested 4 state-of-the-art ASR systems with second language speech-in-noise and found that one, whisper, performed at or above human listener accuracy. However, the content of whisper's responses diverged substantially from human responses, especially at lower signal-to-noise ratios, suggesting both opportunities and limitations for ASR--based speech intelligibility modeling.

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