Digits-In-Noise Hearing Test Using Text-to-Speech and Automatic Speech Recognition: Proof-of-Concept Study.

IF 3 2区 医学 Q1 AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGY
Trends in Hearing Pub Date : 2025-01-01 Epub Date: 2025-10-01 DOI:10.1177/23312165251367625
Mohsen Fatehifar, Kevin J Munro, Michael A Stone, David Wong, Tim Cootes, Josef Schlittenlacher
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引用次数: 0

Abstract

This proof-of-concept study evaluated the implementation of a digits-in-noise test we call the 'AI-powered test' that used text-to-speech (TTS) and automatic speech recognition (ASR). Two other digits-in-noise tests formed the baselines for comparison: the 'keyboard-based test' which used the same configurations as the AI-powered test, and the 'independent test', a third-party-sourced test not modified by us. The validity of the AI-powered test was evaluated by measuring its difference from the independent test and comparing it with the baseline, which was the difference between the Keyboard-based test and the Independent test. The reliability of the AI-powered test was measured by comparing the similarity of two runs of this test and the Independent test. The study involved 31 participants: 10 with hearing loss and 21 with normal-hearing. Achieved mean bias and limits-of-agreement showed that the agreement between the AI-powered test and the independent test (-1.3 ± 4.9 dB) was similar to the agreement between the keyboard-based test and the Independent test (-0.2 ± 4.4 dB), indicating that the addition of TTS and ASR did not have a negative impact. The AI-powered test had a reliability of -1.0 ± 5.7 dB, which was poorer than the baseline reliability (-0.4 ± 3.8 dB), but this was improved to -0.9 ± 3.8 dB when outliers were removed, showing that low-error ASR (as shown with the Whisper model) makes the test as reliable as independent tests. These findings suggest that a digits-in-noise test using synthetic stimuli and automatic speech recognition is a viable alternative to traditional tests and could have real-world applications.

使用文本到语音和自动语音识别的数字噪声听力测试:概念验证研究。
这项概念验证研究评估了使用文本到语音(TTS)和自动语音识别(ASR)的数字噪声测试的实施情况,我们称之为“人工智能测试”。另外两个噪声数字测试构成了比较的基准:“基于键盘的测试”(使用与ai测试相同的配置)和“独立测试”(未经我们修改的第三方测试)。人工智能测试的有效性是通过测量与独立测试的差异,并将其与基线(键盘测试与独立测试的差异)进行比较来评估的。人工智能驱动测试的可靠性是通过比较该测试和独立测试的两次运行的相似性来衡量的。这项研究涉及31名参与者:10名听力受损,21名听力正常。获得的平均偏倚和一致限显示,人工智能驱动测试与独立测试之间的一致性(-1.3±4.9 dB)与基于键盘的测试与独立测试之间的一致性(-0.2±4.4 dB)相似,表明TTS和ASR的添加没有负面影响。人工智能支持的测试的可靠性为-1.0±5.7 dB,低于基线可靠性(-0.4±3.8 dB),但在去除异常值后,该可靠性提高到-0.9±3.8 dB,这表明低误差ASR(如Whisper模型所示)使测试与独立测试一样可靠。这些发现表明,使用合成刺激和自动语音识别的噪声数字测试是传统测试的可行替代方案,可以在现实世界中应用。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Trends in Hearing
Trends in Hearing AUDIOLOGY & SPEECH-LANGUAGE PATHOLOGYOTORH-OTORHINOLARYNGOLOGY
CiteScore
4.50
自引率
11.10%
发文量
44
审稿时长
12 weeks
期刊介绍: Trends in Hearing is an open access journal completely dedicated to publishing original research and reviews focusing on human hearing, hearing loss, hearing aids, auditory implants, and aural rehabilitation. Under its former name, Trends in Amplification, the journal established itself as a forum for concise explorations of all areas of translational hearing research by leaders in the field. Trends in Hearing has now expanded its focus to include original research articles, with the goal of becoming the premier venue for research related to human hearing and hearing loss.
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