An objective metric that predicts speech intelligibility under different types of noise and distortion would be desirable in voice communication. To date, the majority of studies concerning speech intelligibility metrics have focused on predicting the effects of individual noise or distortion mechanisms. This study proposes an objective metric, the spectrogram orthogonal polynomial measure (SOPM), that attempts to predict speech intelligibility for people with normal hearing under adverse conditions. The SOPM metric is developed by extracting features from the spectrogram using Krawtchouk moments. The metric's performance is evaluated for several types of noise (steady-state and fluctuating noise), distortions (peak clipping, center clipping, and phase jitters), ideal time-frequency segregation, and reverberation conditions both in quiet and noisy environments. High correlation (0.97–0.996) is achieved with the proposed metric when evaluated with subjective scores by normal-hearing subjects under various conditions.

中文翻译：

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一种从噪声和失真信号中估计语音清晰度的侵入式方法

在语音通信中，需要一个客观的度量来预测不同类型的噪声和失真下的语音清晰度。迄今为止，大多数关于语音清晰度指标的研究都集中在预测个体噪声或失真机制的影响上。这项研究提出了一个客观的度量，即频谱图正交多项式测量 (SOPM)，它试图预测在不利条件下听力正常的人的语音清晰度。SOPM 度量是通过使用 Krawtchouk 矩从频谱图中提取特征而开发的。该指标的性能针对几种类型的噪声（稳态和波动噪声）、失真（削峰、中心削波和相位抖动）、理想的时频分离、安静和嘈杂环境中的混响条件。当听力正常的受试者在各种条件下用主观评分进行评估时，所提出的指标实现了高相关性（0.97-0.996）。