An Algorithm for Predicting the Intelligibility of Speech Masked by Modulated Noise Maskers
An Algorithm for Predicting the Intelligibility of Speech Masked by Modulated Noise Maskers
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DOI：
10.1109/taslp.2016.2585878
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发表时间：
2016-11-01
影响因子：
5.4
通讯作者：
Taal, Cees H.
中科院分区：
文献类型：
--
作者：
Jensen, Jesper;Taal, Cees H.
在语音处理算法的开发和评估过程中，可理解性听力测试是必要的，尽管它既昂贵又耗时。在本文中，我们提出了一种单耳可理解度预测算法，它有可能取代一些听力测试。该算法与短时客观可理解度（STOI）算法相似，但适用于更大范围的输入信号。与STOI相比，扩展STOI （ESTOI）不假设频带之间相互独立。ESTOI还通过比较有噪声/处理过的语音信号和干净的语音信号的完整400毫秒的频谱图来结合频谱相关性。因此，除了经过时频加权处理的噪声信号外，ESTOI还能够准确地预测受时间高度调制噪声源污染的语音的可理解性。我们表明，ESTOI可以通过将短时间谱图正交分解为可理解性子空间来解释，即根据谱图特征对可理解性的重要性进行排序。该算法的免费MATLAB实现可在http://kom.aau.dk/similar to jje/上用于非商业用途。
Intelligibility listening tests are necessary during development and evaluation of speech processing algorithms, despite the fact that they are expensive and time consuming. In this paper, we propose a monaural intelligibility prediction algorithm, which has the potential of replacing some of these listening tests. The proposed algorithm shows similarities to the short-time objective intelligibility (STOI) algorithm, but works for a larger range of input signals. In contrast to STOI, extended STOI (ESTOI) does not assume mutual independence between frequency bands. ESTOI also incorporates spectral correlation by comparing complete 400-ms length spectrograms of the noisy/processed speech and the clean speech signals. As a consequence, ESTOI is also able to accurately predict the intelligibility of speech contaminated by temporally highly modulated noise sources in addition to noisy signals processed with time-frequency weighting. We show that ESTOI can be interpreted in terms of an orthogonal decomposition of short-time spectrograms into intelligibility subspaces, i.e., a ranking of spectrogram features according to their importance to intelligibility. A free MATLAB implementation of the algorithm is available for noncommercial use at http://kom.aau.dk/similar to jje/.