Advances in Acoustic Noise Tracking for Robust In-Vehicle Speech Systems

Abstract

Speech systems work reasonably well under homogeneous acoustic environmental conditions but become fragile in practical applications involving real-world environments (e.g., in-car, broadcast news, digital archives, etc.) where the audio stream contains multi-environment characteristics. To date, most approaches dealing with environmental noise in speech systems are based on assumptions concerning the noise, rather than exploring and characterizing the nature of the noise. In this chapter, we present our recent advances in the formulation and development of an in-vehicle environmental sniffing framework previously presented in [1,2,3,4]. The system is comprised of different components 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 change points. The second goal is to organize this knowledge in an effective manner to allow intelligent decisions to direct subsequent speech processing systems. After presenting our proposed in-vehicle environmental sniffing framework, we consider future directions and present discussion on supervised versus unsupervised noise clustering, and closed-set versus open-set noise classification.

This work was supported in part by DARPA through SPA WAR under Grant No. N66001-002-8906, from SPA WAR under Grant No. N66001-03-1-8905, m part by NSF under Cooperative Agreement No. IIS-9817485.