No abstract

Numerous efforts have focused on the problem of reducing the impact of noise on the performance of various speech systems such as speech recognition, speaker recognition, and speech coding. These approaches consider alternative speech features, improved speech modeling, or alternative training for acoustic speech models. This study presents an alternative viewpoint by approaching the same problem from the noise perspective. Here, a framework is developed to analyze and use the noise information available for improving performance of speech systems. The proposed framework focuses on explicitly modeling the noise and its impact on speech system performance in the context of speech enhancement. The framework is then employed for development of a novel noise tracking algorithm for achieving better speech enhancement under highly evolving noise types. The first part of this study employs a noise update rate in conjunction with a target enhancement algorithm to evaluate the need for tracking in many enhancement algorithms.

No abstract

No abstract

No abstract