Joint analysis of vocal tract length and temporal information for robust speech recognition

This paper presents a joint analysis approach to address the acoustic feature normalization for robust speech recognition. The variations in acoustic environments and speakers are the major challenge for speech recognition. The conventional normalizations of these two variations are separately processed, applying the speaker normalization with an assumption of a noise free condition and applying the noise compensation with an assumption of speaker independency, and thus resulting in a suboptimal performance. The proposed joint analysis approach simultaneously considers the vocal tract length normalization and averaged temporal information of cepstral features. In a data-driven manner, the Gaussian mixture model is used to estimate the conditional parameters in the joint analysis. Experimental results show that the proposed approach achieves a substantial improvement.