A computationally constrained optimization framework for implementation and tuning of speech enhancement systems

In this work, we propose an optimization framework for tuning the parameters of a speech enhancement system to maximize its performance while constraining its computational complexity imposed by a target platform. Some parameters allow for enabling or disabling certain algorithmic components of the system, effectively guiding the implementation effort. The speech enhancement system is deployed in a speech recognition front-end and in a full-duplex telephony system. The optimization variables are the parameters of the system and the performance is measured using phone accuracy rate and mean opinion score, respectively. The problem is then a nonlinear program of combinatorial nature which is solved efficiently using a genetic algorithm. The results show improvement in performance over common tuning and implementation strategies.