Noise tracking for speech systems in adverse environments

In the design of speech systems, the primary focus is the speech oriented task with the secondary emphasis on sustaining performance under varying operating conditions. Variation in environmental conditions is one of the most important factors that impact speech system performance. In this study, we propose a framework for noise tracking. The proposed noise tracking algorithm is compared with Martin’s [1] and Cohen’s [2] estimation scheme’s for speech enhancment in non-stationary noise conditions. The noise tracking scheme is evaluated over a corpus of three noise types including Babble (BAB), Large Crowd (LCR), and Machine Gun (MGN). The noise modeling scheme for tracking results in a measureable level of improvement for all the noise types (e.g., a 13.7% average relative improvement in Itakura-Saito(IS) measure over 9 noise conditions). This framework is therefore useful for speech applications requiring effective performance for nonstationary environments.