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在安静和噪音下使用深度神经网络自动化语音音计

Hadrien Jean¹, Nicolas Wallaert^1,2, Antoine Dreumont³

¹R&D Department, My Medical Assistant SAS, 51100 Reims, France.

|February 26, 2025

概括

一个自动语音识别 (ASR) 系统准确地在安静和噪音中对语音理解进行评分,与人类专家相匹配. 这种深度神经网络为临床和研究环境中的听力评估提供了可靠,高效的工具.

科学领域:

听力学听力学是指听力学.
语音科学语言科学
医疗保健中的人工智能

背景情况:

综合性听力评估包括语音理解评估.
当前的语音听力测量需要专业人员进行耗时的手动评分.
需要自动评分方法来提高效率.

研究的目的:

开发和验证一个自动语音识别 (ASR) 系统,用于语音音计的语音级评分.
评估ASR系统在临床环境中的性能和可靠性.
为了比较ASR系统的评分准确度和测试重试可靠性与手动评分.

主要方法:

开发了一个基于深度神经网络的自动语音识别 (ASR) 系统.
使用法语语音材料训练ASR系统 (Lafon的耳列表,Dodelé的logatoms).
评估ASR系统的性能和可靠性与正常听力和听力受损的听众在安静和杂的条件下.

主要成果:

该ASR系统在统计学上表现出与专家听力专业人员手动评分相似的性能.
在安静和杂的听力条件下,自动评分的准确性是一致的.
自动评分的测试-重新测试可靠性与手动评分的可靠性非常接近.

结论:

开发的基于深度神经网络的ASR系统为语音听力测量提供了准确可靠的方法.
该自动化系统已被验证用于临床实践和研究中的听力评估.
该ASR系统提供了一种有效的替代手动评分,改善语音理解的评估.

关键词:

自动语音识别自动化语音识别深度神经网络是一个神经网络.机器学习是机器学习.语音听力测量语音听力测量语音在噪音中的语言.

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