Machine Learning Based Approach to Assess Denoised Speech
Anis Ben Aicha · Procedia Computer Science · 2019
Purpose: Many objective measures are developed to assess speech in specific contexts such as speech coding, transmission, etc. Speech enhancement is one of emergent speech technology applications. Yet, there is no objective standard to assess denoised speech. Indeed, the objective criteria are developed mainly to assess speech for specific context i.e., speech coding, speech transmission, etc. They are not developed specially to assess denoised speech. Some attempts are developed in the literature based on a linear combination of existing measures. Method: Even such approaches permit a promising performances, they reach their limits. To overcome these limits, we propose to address the problem from the point of view of classification instead of the point of view of regression. The assessed speech utterance is divided into frames. Each frame is evaluated using one of conventional criteria. Hence, a feature vector is constructed. The objective denoising speech assessment becomes a classical classification problem. Results: Unlike traditional criteria, the proposed method can give a significant objective score directly interpreted as an estimation of real mean opinion score. Conclusion: It is shown in this study that is possible to predict the subjective mean opinion score with acceptable and fairly sensitivity, specificity, precision and accuracy.