tailieunhanh - Digital Signal Processing Handbook P27

This section addresses the inverse problem in robust speech processing. A problem that speaker and speech recognition systems regularly encounter in the commercialized applications | Mammone . Zhang X. Robust Speech Processing as an Inverse Problem Digital Signal Processing Handbook Ed. Vijay K. Madisetti and Douglas B. Williams Boca Raton CRC Press LLC 1999 1999 by CRC Press LLC 27 Robust Speech Processing as an Inverse Problem Richard J. Mammone Rutgers University Xiaoyu Zhang Rutgers University Introduction Speech Production and Spectrum-Related Parameterization Template-Based Speech Processing Robust Speech Processing Affine Transform Transformation of Predictor Coefficients Deterministic Convolutional Channel as a Linear Transform Additive Noise as a Linear Transform Affine Transform of Cepstral Coefficients Parameters of Affine Transform Correspondence of Cepstral Vectors References Introduction This section addresses the inverse problem in robust speech processing. A problem that speaker and speech recognition systems regularly encounter in the commercialized applications is the dramatic degradation of performance due to the mismatch of the training and operating environments. The mismatch generally results from the diversity of the operating environments. For applications over the telephone network the operating environments may vary from offices and laboratories to household places and airports. The problem becomes worse when speech is transmitted over the wireless network. Here the system experiences cross-channel interferences in addition to the channel and noise degradations that exist in the regular telephone network. The key issue in robust speech processing is to obtain good performance regardless of the mismatch in the environmental conditions. The inverse problem in this sense refers to the process of modeling the mismatch in the form of a transformation and resolving it via an inverse transformation. In this section we introduce the method of modeling the mismatch as an affine transformation. Before getting into the details of the inverse problem in robust speech processing