Finite State Transducer Framework Research Articles

Morpholexical and Discriminative Language Models for Turkish Automatic Speech Recognition

This paper introduces two complementary language modeling approaches for morphologically rich languages aiming to alleviate out-of-vocabulary (OOV) word problem and to exploit morphology as a knowledge source. The first model, morpholexical language model, is a generative $n$ -gram model, where modeling units are lexical-grammatical morphemes instead of commonly used words or statistical sub-words. This paper also proposes a novel approach for integrating the morphology into an automatic speech recognition (ASR) system in the finite-state transducer framework as a knowledge source. We accomplish that by building a morpholexical search network obtained by the composition of lexical transducer of a computational lexicon with a morpholexical language model. The second model is a linear reranking model trained discriminatively with a variant of the perceptron algorithm using morpholexical features. This variant of the perceptron algorithm, WER-sensitive perceptron, is shown to perform better for reranking $n$ -best candidates obtained with the generative model. We apply the proposed models in Turkish broadcast news transcription task and give experimental results. The morpholexical model leads to an elegant morphology-integrated search network with unlimited vocabulary. Thus, it is highly effective in alleviating OOV problem and improves the word error rate (WER) over word and statistical sub-word models by 1.8% and 0.4% absolute, respectively. The discriminatively trained morpholexical model further improves the WER of the system by 0.8% absolute.

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.