Set Of Context-free Rules Research Articles

Characterization of Ordered Semigroups Generating Well Quasi-Orders of Words

The notion of a quasi-order generated by a homomorphism from the semigroup of all words onto a finite ordered semigroup was introduced by Bucher et al. (Theor. Comput. Sci. 40, 131–148 1985). It naturally occurred in their studies of derivation relations associated with a given set of context-free rules, and they asked a crucial question, whether the resulting relation is a well quasi-order. We answer this question in the case of the quasi-order generated by a semigroup homomorphism. We show that the answer does not depend on the homomorphism, but it is a property of its image. Moreover, we give an algebraic characterization of those finite semigroups for which we get well quasi-orders. This characterization completes the structural characterization given by Kunc (Theor. Comput. Sci. 348, 277–293 2005) in the case of semigroups ordered by equality. Compared with Kunc’s characterization, the new one has no structural meaning, and we explain why that is so. In addition, we prove that the new condition is testable in polynomial time.

Statistical modeling of phonological rules through linguistic hierarchies

This paper describes our research aimed at acquiring a generalized probability model for alternative phonetic realizations in conversational speech. For all of our experiments, we utilize the summit landmark-based speech recognition framework. The approach begins with a set of formal context-dependent phonological rules, applied to the baseforms in the recognizer’s lexicon. A large speech corpus is phonetically aligned using a forced recognition procedure. The probability model is acquired by observing specific realizations expressed in these alignments. A set of context-free rules is used to parse words into substructure, in order to generalize context-dependent probabilities to other words that share the same sub-word context. The model maps phones to sub-word units probabilistically in a finite state transducer framework, capturing phonetic predictions based on local phonemic, morphologic, and syllabic contexts. We experimented within two domains: the mercury flight reservation domain and the jupiter weather domain. The baseline system used the same set of phonological rules for lexical expansion, but with no probabilities for the alternates. We achieved 14.4% relative reduction in concept error rate for jupiter and 16.5% for mercury.

The use of subword linguistic modeling for multiple tasks in speech recognition

Over the past several years, I have been conducting research on subword modeling in speech recognition. The research is most specifically aimed at the difficult task of identifying and characterizing unknown words, although the proposed framework also has utility in other recognition tasks such as phonological and prosodic modeling. The approach exploits the linguistic substructure of words by describing graphemic, phonemic, phonological, syllabic, and morphemic constraints through a set of context-free rules, and supporting the resulting parse trees with a corpus-trained probability model. A derived finite state transducer representation forms a natural means for integrating the trained model into a recognizer search. This paper describes several research projects I have been engaged in, together with my students and associates, aimed at exploring ways in which recognition tasks can benefit from such formal modeling of word substructure. These include phonological modeling, hierarchical duration modeling, sound-to-letter and letter-to-sound mapping, and automatic acquisition of unknown words in a speech understanding system. Results of several experiments in these areas are summarized here.