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We propose a spatial subtraction array (SSA) and known noise superimposition to achieve a robust hands-free speech recognition under noisy environments. In the proposed SSA, noise reduction is achieved by subtracting the estimated noise power spectrum from the target speech power spectrum to be enhanced in the mel-scale filter bank domain. This offers a realization of error-robust spatial spectral subtraction with few computational complexities. In addition, we introduce known noise superimposition technique in the mel-scale filter bank domain, and utilize the matched acoustic model for the known noise. This can compensate the acoustic model mismatch and mask the residual noise component in SSA. The experimental results obtained under a real environment reveal that word accuracy of the proposed method is greater than that of the conventional method even when the target user moves between -10 and +10 degrees around the microphone array. Recently, hands-free speech communication is becoming increasingly necessary for teleconferences, in-car phones, and PC-based IP telephony. Since speech signals acquired by a distant microphone are generally corrupted by ambient noise, noise reduction is indispensable for ensuring speech quality. To investigate the effect of noise reduction on speech quality, the subjective quality assessment of noisy speech signab and noise reduced speech signals was performed. This result confirmed that the difference of noise types gives little effect on the subjective quality and the subjective quality is more sensitive to the speech distortion than the residual noise. This paper also investigated the applicability of the PEsQ to the objective quality assessment o f noise reduction algorithms. The experimental result showed that the objective quality correlates well with the subjective quality. However, there was the case that the objective quality is lower than the subjective quality in the specific noise reduction algorithms. 28

It is essential to ensure a satisfactory QoS (Quality of Service) when offering a speech communication system with a noise reduction algorithm. In this paper, we propose a new obejective test methodology for noise-reduced speech that estimates word intelligibility by using a distortion measure. Experimental results confirmed that the proposed methodology gives an accurate estimate with independence of noise reduction algorithms and noise types.

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