| Time-frequency analysis techniques, such as wavelet decomposition and Gabor filtering, are a tool for efficient coding of short-term acoustical features, and so are fundamental to acoustic pattern classification and speech recognition.;This thesis addresses issues of efficiency and robustness in the design and implementation of acoustic signal processors and small-vocabulary speech recognition systems for applications where power dissipation and integration density are primary design constraints. We couple time-frequency signal representations with massively parallel architectures using analog VLSI technology to design compact special-purpose systems with power efficiency surpassing conventional DSPs.;We present the design of, and results from, several prototyped VLSI systems, including processors for time-frequency decomposition and template-correlation-based acoustic transient pattern classification. We present methods for automatically training a template correlator and discuss potential research directions for this architecture, including biological modeling and continuous-speech recognition. |
