Eecient Algorithms for Speech Recognition Thesis Committee

Advances in speech technology and computing power have created a surge of interest in the practical application of speech recognition. However, the most accurate speech recognition systems in the research world are still far too slow and expensive to be used in practical, large vocabulary continuous speech applications. Their main goal has been recognition accuracy, with emphasis on acoustic and language modelling. But practical speech recognition also requires the computation to be carried out in real time within the limited resources|CPU power and memory size|of commonly available computers. There has been relatively little work in this direction while preserving the accuracy of research systems. In this thesis, we focus on e cient and accurate speech recognition. It is easy to improve recognition speed and reduce memory requirements by trading away accuracy, for example by greater pruning, and using simpler acoustic and language models. It is much harder to improve both the recognition speed and reduce main memory size while preserving the accuracy. This thesis presents several techniques for improving the overall performance of the CMU Sphinx-II system. Sphinx-II employs semi-continuous hidden Markov models for acoustics and trigram language models, and is one of the premier research systems of its kind. The techniques in this thesis are validated on several widely used benchmark test sets using two vocabulary sizes of about 20K and 58K words. The main contributions of this thesis are an 8-fold speedup and 4-fold memory size reduction over the baseline Sphinx-II system. The improvement in speed is obtained from the following techniques: lexical tree search, phonetic fast match heuristic, and global best path search of the word lattice. The gain in speed from the tree search is about a factor of 5. The phonetic fast match heuristic speeds up the tree search by another factor of 2 by nding the most likely candidate phones active at any time. Though the tree search incurs some loss of accuracy, it also produces compact word lattices with low error rate which can be rescored for accuracy. Such a rescoring is combined with the best path algorithm to nd a globally optimum path through a word lattice. This recovers the original accuracy of the baseline system. The total recognition time is about 3 times real time for the 20K task on a 175MHz DEC Alpha workstation. The memory requirements of Sphinx-II are minimized by reducing the sizes of the acoustic and language models. The language model is maintained on disk and bigrams and trigrams are read in on demand. Explicit software caching mechanisms e ectively overcome the disk access latencies. The acoustic model size is reduced by simply truncating precision of probability values to 8 bits. Several other engineering solutions, not explored in this thesis, can be applied to reduce memory requirements further. The memory size for the 20K task is reduced to about 30-40MB.