Data-driven vector clustering for low-memory footprint ASR
It is important to produce automatic speech recognition (ASR) systems that use as few computational and memory resources as possible, especially in low-memory/low-power environments such as for personal digital assistants. One way to achieve this is through parameter quantization. In this work, we compare a variety of novel subvector clustering procedures for ASR system parameter quantization. Specifically, we look at systematic data-driven subvector selection techniques based on entropy minimization, and compare performance on a 150-word isolated word speech recognition task. While the optimal entropy-minimizing quantization methods are intractable, we show that although several of our heuristic techniques are elaborate in their attempt to approximate the optimal clustering, a simple scalar quantization scheme using separate codebooks performs remarkably well.