Intrusive Quality Measurement of Noisy and Enhanced Speech based on i-Vector Similarity
Anderson Raymundo Avila, Jahangir Alam, Douglas D. O’Shaughnessy, Tiago Henrique Falk · 2019
In this paper, the i-vector framework is investigated as an intrusive quality measure for noisy and enhanced speech. While widely used across numerous speech applications, the potential of using i-vectors to summarize the quality of a speech recording has been overlooked. This paper aims to fill this gap. We show that the i-vector framework is well-suited for assessing speech signal quality, surpassing well-established instrumental measures such as the Perceptual Evaluation of Speech Quality (PESQ) and Perceptual Objective Listening Quality Analysis (POLQA). Three datasets are used in our experiments. First, the TIMIT database is used to train the i-vector extractor on clean speech. To evaluate the proposed method, the noisy speech corpus (NOIZEUS) and the evaluation set of the 2014 IEEE REVERB challenge are used with both containing subjective ratings of perceived quality. Correlations with mean opinion scores (MOS) as high as 0.90 are achieved.