Non-Intrusive Objective Evaluation Of Speech Quality In Noisy Condition

It is very difficult, if not impossible, to obtain a clean reference signal of a noisy speech recorded in a practical environment. As a result, intrusive methods that evaluate the quality of speech signal with the help of a clean reference signal has little value in real world applications. In this paper, we investigate the effectiveness of data-driven non-intrusive method for assessing quality of speech without using clean reference signal. In the proposed method, a support vector machine based classifier is trained using a labelled dataset and then the classifier provides speech assessment rating on unknown speech signals. The obtained results have been evaluated against the intrusive PESQ score. The results indicate that the proposed technique performs better than the state-of-the-art nonintrusive methods on the same test data set.