A Novel Method for Intelligibility Assessment of Nonlinearly Processed Speech in Spaces Characterized by Long Reverberation Times

Abstract

Objective assessment of speech intelligibility is a complex task that requires taking into account a number of factors such as different perception of each speech sub-bands by the human hearing sense or different physical properties of each frequency band of a speech signal. Currently, the state-of-the-art method used for assessing the quality of speech transmission is the speech transmission index (STI). It is a standardized way of objectively measuring the quality of, e.g., an acoustical adaptation of conference rooms or public address systems. The wide use of this measure and implementation of this method on numerous measurement devices make STI a popular choice when the speech-related quality of rooms has to be estimated. However, the STI measure has a significant drawback which excludes it from some particular use cases. For instance, if one would like to enhance speech intelligibility by employing a nonlinear digital processing algorithm, the STI method is not suitable to measure the impact of such an algorithm, as it requires that the measurement signal should not be altered in a nonlinear way. Consequently, if a nonlinear speech enhancing algorithm has to be tested, the STI—a standard way of estimating speech transmission cannot be used. In this work, we would like to propose a method based on the STI method but modified in such a way that it makes it possible to employ it for the estimation of the performance of the nonlinear speech intelligibility enhancement method. The proposed approach is based upon a broadband comparison of cumulated energy of the transmitted envelope modulation and the received modulation, so we called it broadband STI (bSTI). Its credibility with regard to signals altered by the environment or nonlinear speech changed by a DSP algorithm is checked by performing a comparative analysis of ten selected impulse responses for which a baseline value of STI was known.

Analytics and Applications of Audio and Image Sensing Techniques

“…In the next paper [ 6 ], A. Kurowski, J. Kotus, P. Odya, and B. Kostek propose a novel method for assessing the intelligibility of nonlinearly processed speech in reverberant spaces. This method is based on STI (speech transmission index), which is a popular measure of speech transmission quality of linearly processed signals.…”

Detecting Lombard Speech Using Deep Learning Approach

“…This refers to the case when the ASR system is not trained on Lombard samples but only on “normal” (neutral) utterances. Moreover, speech recordings are conditioned upon the space constraints in which they are registered or collected [ 25 ]. In contrast, in the case of human-to-human communication, the Lombard effect improves speech intelligibility in noise [ 26 , 27 , 28 ], while for human-to-computer communication, it increases the already high variability of the speech signal.…”