Combined front-end signal processing for in-vehicle speech systems

In this paper, we investigate the integration of two processing methods to improve speech quality for invehicle speech systems: multi-sensor beamforming and constrained iterative (Auto-LSP) speech enhancement. The intent is to establish an intelligent microphone array processing scheme in high noise environments by considering the effectiveness of a multi-sensor beamformer method and the Auto-LSP single channel speech enhancement method. The goal therefore is to design a system where the strengths of one method help compensate any potential weaknesses of the other. The noise cancellation method is an acoustic beamformer designed and constructed using a linear microphone array. The speech enhancement method is the constrained iterative Auto-LSP approach, previously considered for single channel enhancement. After establishing the combined processing scheme, evaluations are performed using speech and acoustic noise data collected in vehicles. Noise suppression levels by the beamformer is established for different road noise conditions. Quality improvement from the enhancement scheme is assessed using objective speech quality measures over a test speech corpus using TIMIT data. The results show that while beamforming alone can suppress background noise levels, the combination of beamforming and constrained enhancement can provide as much as a 63% improvement in objective quality, suggesting a potential single comprehensive solution for in-vehicle speech systems.