Speech Recognition by Composition of Weighted Finite Automata

We present a general framework based on weighted finite automata and weighted finite-state transducers for describing and implementing speech recognizers. The framework allows us to represent uniformly the information sources and data structures used in recognition, including context-dependent units, pronunciation dictionaries, language models and lattices. Furthermore, general but efficient algorithms can used for combining information sources in actual recognizers and for optimizing their application. In particular, a single composition algorithm is used both to combine in advance information sources such as language models and dictionaries, and to combine acoustic observations and information sources dynamically during recognition.Comment: 24 pages, uses psfig.st

Morphological annotation of Korean with Directly Maintainable Resources

This article describes an exclusively resource-based method of morphological annotation of written Korean text. Korean is an agglutinative language. Our annotator is designed to process text before the operation of a syntactic parser. In its present state, it annotates one-stem words only. The output is a graph of morphemes annotated with accurate linguistic information. The granularity of the tagset is 3 to 5 times higher than usual tagsets. A comparison with a reference annotated corpus showed that it achieves 89% recall without any corpus training. The language resources used by the system are lexicons of stems, transducers of suffixes and transducers of generation of allomorphs. All can be easily updated, which allows users to control the evolution of the performances of the system. It has been claimed that morphological annotation of Korean text could only be performed by a morphological analysis module accessing a lexicon of morphemes. We show that it can also be performed directly with a lexicon of words and without applying morphological rules at annotation time, which speeds up annotation to 1,210 word/s. The lexicon of words is obtained from the maintainable language resources through a fully automated compilation process

A 6 mW, 5,000-Word Real-Time Speech Recognizer Using WFST Models

We describe an IC that provides a local speech recognition capability for a variety of electronic devices. We start with a generic speech decoder architecture that is programmable with industry-standard WFST and GMM speech models. Algorithm and architectural enhancements are incorporated in order to achieve real-time performance amid system-level constraints on internal memory size and external memory bandwidth. A 2.5 × 2.5 mm test chip implementing this architecture was fabricated using a 65 nm process. The chip performs a 5,000 word recognition task in real-time with 13.0% word error rate, 6.0 mW core power consumption, and a search efficiency of approximately 16 nJ per hypothesis.Quanta Computer (Firm)Irwin Mark Jacobs and Joan Klein Jacobs Presidential Fellowshi

Practical experiments with regular approximation of context-free languages

Several methods are discussed that construct a finite automaton given a context-free grammar, including both methods that lead to subsets and those that lead to supersets of the original context-free language. Some of these methods of regular approximation are new, and some others are presented here in a more refined form with respect to existing literature. Practical experiments with the different methods of regular approximation are performed for spoken-language input: hypotheses from a speech recognizer are filtered through a finite automaton.Comment: 28 pages. To appear in Computational Linguistics 26(1), March 200