Custom arithmetic is a novel and successful technique to reduce the computation and resource utilization of ASR systems running on mobile devices. It represents all floating-point numbers by integer indices and substitutes a sequence of table lookups for all arithmetic operations. The first and crucial step in custom arithmetic design is to quantize system variables, preferably to low precision. This paper explores several techniques to quantize variables with high entropy, including a reordering of Gaussian computation and a normalization of Viterbi search. Furthermore, a discrimina-tively inspired distortion measure is investigated for scalar quantization to better maintain recognition accuracy. Experiments on an isolated word recognition show that each system variable can be scalar quantized to less than 8 bits using a standard quantization method, except for the alpha probability in Viterbi search which requires 10 bits. However, using our normalization and discriminative distortion measure, the forward probability can be quantized to 9 bits, thereby halving the corresponding lookup table size. This greatly reduces the memory bandwidth and enables the implementation of custom arithmetic on ASR systems.
展开▼
