Tuning methodology for speech enhancement algorithms using a simulated conversational database and perceptual objective measures
Daniele Giacobello, Jason Wung, Ramin Pichevar, Joshua D. Atkins · 2014
In this paper, we propose a formal methodology for tuning the parameters of a single-microphone speech enhancement system for hands-free devices. The tuning problem is formulated as a large-scale nonlinear programming problem that is solved by a genetic algorithm to determine the global solution. A conversational speech database is automatically generated by modeling the interactivity in telephone conversations, and perceptual objective quality measures are used as the optimization criteria for the automated tuning over the generated database. A subjective listening test is then performed by comparing the automatically tuned system based on objective criteria to the system tuned by expert human listeners. Subjective and objective evaluation result shows that the proposed automated tuning methodology greatly improves the enhanced speech quality, potentially saving resources over manual evaluation, speeding up development and deployment time, and guiding the algorithmic design.