A Fast and Memory-Efficient N-gram Language Model Lookup Method for Large Vocabulary Continuous Speech Recognition

Xiaolong Li and Yunxin Zhao

Abstract

Recently, minimum perfect hashing (MPH)-based language model (LM) lookup methods have been proposed for fast access of N-gram LM scores in lexical-tree based LVCSR (large vocabulary continuous speech recognition) decoding. Methods of node-based LM cache and LM context pre-computing (LMCP) have also been proposed to combine with MPH for further reduction of LM lookup time. Although these methods are effective, LM lookup still takes a large share of overall decoding time when trigram LM lookahead (LMLA) is used for lower word error rate than unigram or bigram LMLAs. Besides computation time, memory cost is also an important performance aspect of decoding systems. Most speedup methods for LM lookup obtain higher speed at the cost of increased memory demand, which makes system performance unpredictable when running on computers with smaller memory capacities. In this paper, an order-preserving LM context pre-computing (OPCP) method is proposed to achieve both fast speed and small memory cost in LM lookup. By reducing hashing operations through order-preserving access of LM scores, OPCP cuts down LM lookup time effectively. In the meantime, OPCP significantly reduces memory cost because of reduced size of hashing keys and the need for only last word index of each N-gram in LM storage. Experimental results are reported on two LVCSR tasks (Wall Street Journal 20K and Switchboard 33K) with three sizes of trigram LMs (small, medium, large). In comparison with abovementioned existing methods, OPCP reduced LM lookup time from about 30–80% of total decoding time to about 8–14%, without any increase of word error rate. Except for the small LM, the total memory cost of OPCP for LM lookup and storage was about the same or less than the original N-gram LM storage, much less than the compared methods. The time and memory savings in LM lookup by using OPCP became more pronounced with the increase of LM size.

Details

Publication typeArticle
Published inComputer Speech and Language
Pages1-25
Volume21
PublisherElsevier
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