A robust mel-scale subband voice activity detector for a car platform

Voice-controlled devices provide a smart solution to operate addon appliances in a car. Although, speech recognition appears as a key technology to produce useful end-user interfaces, the amount of acoustic disturbances existing in automotive platforms usually prevents satisfactory results. In most of the cases, noise reduction techniques involving a Voice Activity Detector (VAD) are required. Through this paper, a robust method for speech detection under the influence of noise and reverberation in an automobile environment is proposed. This method determines a consistent speech/non-speech discrimination by means of a set of OrderStatistics Filters (OSFs) applied to the log-energies associated to a mel-scale based subband division. The paper also includes an extensive performance evaluation of the algorithm using AURORA3 database recordings. According to our simulation results, the proposed algorithm shows on average a significantly better performance than standard VADs such as ITU-G.729B, GSM-AMR or ETSI-AFE, and other recently reported methods.