Non-intrusive Estimation of Noisiness as a Perceptual Quality Dimension of Transmitted Speech

This article presents a new approach to the non-intrusive quality estimation of transmitted speech. Traditional estimation methods exhibit limitations to providing diagnostic information and for practical monitoring purposes. The new approach merges solutions to overcome the existing limitations and intends to provide a new user-friendly estimator. We present an overview and the planned structure of the proposed model. In order to provide diagnostic information, the method of assessing perceptual quality-relevant dimensions is applied. One of these quality dimensions is Noisiness, which describes degradations like background noise, circuit noise, or coding noise. As a fundamental component of the proposed model, a non-intrusive parametric Noisiness estimator is presented. The estimator is based on nine different features extracted from the output signal only. Using a linear regression, the features are mapped onto the Noisiness. The Noisiness estimator is trained on two and tested on three individual subjective databases. In addition, the performance of the resulting estimator is compared to the diagnostic intrusive estimator DIAL (Diagnostic Intrusive Assessment of Listening quality). The results prove that the presented estimator provides high reliability and indicate the applicability and value for non-intrusive diagnostic quality estimation.