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This work proposes and implements a novel and robust adaptive speech enhancement system, which contains both time domain and frequency domain beamformers using H∞ filtering approach to provide a clean and undisturbed speech waveform and improve the speech recognition rate in vehicle environments. A microphone array data acquisition hardware is also designed and implemented for the proposed speech enhancement system. Mutually matched microphones are needed for traditional multidimensional noise reduction methods, but this requirement is not practical for consumer applications from the cost standpoint. To overcome this issue, the proposed system adapts the mismatch dynamics to maintain the theoretical performance allowing unmatched microphones to be used in an array. Furthermore, to achieve a high speech recognition performance, the speech recognizer is usually required to be retrained for different vehicle environments due to different noise characteristics and channel effects. The proposed system using the H∞ filtering approach, which makes no assumptions about noise and disturbance, is robust to the modeling error in a channel recovery process. Consequently, the real vehicular experimental results show that the proposed frequency domain beamformer provides a satisfactory speech recognition performance without the need to retrain the speech recognizer. [ABSTRACT FROM AUTHOR]

Human-computer interaction (HCI) using speech communication is becoming increasingly important, especially in driving where safety is the primary concern. Knowing the speaker's location (i.e., speaker localization) not only improves the enhancement results of a corrupted signal, but also provides assistance to speaker identification. Since conventional speech localization algorithms suffer from the uncertainties of environmental complexity and noise, as well as from the microphone mismatch problem, they are frequently not robust in practice. Without a high reliability, the acceptance of speech-based HCI would never be realized. This work presents a novel speaker's location detection method and demonstrates high accuracy within a vehicle cabinet using a single linear microphone array. The proposed approach utilize Gaussian mixture models (GMM) to model the distributions of the phase differences among the microphones caused by the complex characteristic of room acoustic and microphone mismatch. The model can be applied both in near-field and far-field situations in a noisy environment. The individual Gaussian component of a GMM represents some general location-dependent but content and speaker-independent phase difference distributions. Moreover, the scheme performs well not only in nonline-of-sight cases, but also when the speakers are aligned toward the microphone array but at difference distances from it. This strong performance can be achieved by exploiting the fact that the phase difference distributions at different locations are distinguishable in the environment of a car. The experimental results also show that the proposed method outperforms the conventional multiple signal classification method (MUSIC) technique at various SNRs. [ABSTRACT FROM AUTHOR]