Improved Voice Activity Detection in the Presence of Passing Vehicle Noise

Voice activity detection (VAD) is an important enabling technology for a variety of speech-based applications including speech recognition, speech encoding, and hands-free telephony. The primary function of a voice activity detector is to provide an indication of speech presence in order to facilitate speech processing as well as possibly provide delimiters for the beginning and end of a speech segment. While VAD is often quite effective in benign acoustical environments, e.g. a conference room, it tends be less accurate in vehicular environments due to the strong noise present in the automobile cabin. Historically, vehicular voice activity detectors have relied on the fact that the noise in the automobile cabin tends to be stationary over long periods of time and, as such, can be suppressed to a large extent by an adaptive filter with coefficients obtained during non-speech periods [1]. While adaptive filtering does tend to improve the accuracy of VAD in the automotive environment, it is not capable of suppressing short-term nonstationary noise signals, e.g. noise from passing vehicles. In driving scenarios with frequent passing vehicle events, traditional vehicular voice activity detectors may suffer from an unacceptable number of false detections of speech and, as a consequence, the overall performance of the speech application may be significantly degraded.