Voice quality assessment using classification trees

Conventional listening-test based voice quality measurement is performed "offline" and costly, and the test results vary from test to test due to a variety of factors. Signal processing based, "objective" voice quality measurement can be performed economically in real-time. Deployed online, automatic voice quality measurement provides an efficient means for monitoring voice quality, and can be integrated with network intelligence to provide end-to-end voice quality assurance. In this paper, we describe using classification trees to estimate mean opinion scores (MOS) from features extracted from the speech signal. Experimental results demonstrate that the approach outperforms ITU-T P.862 (PESQ), the state-of-art standard for objective voice quality measurement.