Environmental sniffing: noise knowledge estimation for robust speech systems

We propose a framework for extracting knowledge about environmental noise from an input audio sequence and organizing this knowledge for use by other speech systems. To date, most approaches dealing with environmental noise in speech systems are based on assumptions about the noise, or differences in the collection of and training on a specific noise condition, rather than exploring the nature of the noise. We are interested in constructing a new speech framework, entitled environmental sniffing, to detect, classify and track acoustic environmental conditions. The first goal of the framework is to seek out detailed information about the environmental characteristics instead of just detecting environmental changes. The second goal is to organize this knowledge in an effective manner to allow smart decisions to direct other speech systems. Our current framework uses a number of speech processing modules including the Teager energy operator (TEO) and a hybrid algorithm with T/sup 2/-BIC segmentation, noise language modeling and GMM classification in noise knowledge estimation. We define a new information criterion that incorporates the impact of noise on environmental sniffing performance. We use an in-vehicle speech and noise environment as a test platform for our evaluations and investigate the integration of environmental sniffing into an automatic speech recognition (ASR) engine in this environment. Noise classification experiments show that the hybrid algorithm achieves an error rate of 25.51%, outperforming a baseline system by an absolute 7.08%.