This paper proposes a novel combined compound splitting and phrase recombination method that optimizes the composition of the speech recognition lexicon for a given domain. Data-driven compound word splitting is followed by iterative recombination of high frequency combinations. Language model perplexity and size are the criteria used to identify a balance between compound decomposition, which reduces OOV, and lexical unit recombination, which packs additional context into a fixed-size vocabulary. The method provides a basis for lexicon design for a LVCSR system on the domain of German parliamentary speeches that is to be used as the foundation of a spoken document information retrieval system. The approach achieves a 35% reduction in OOV without a prohibitively large sacrifice in recognition performance. 1. INTRODUCTION The convention of adopting the orthographic word as the basic unit in the LVCSR lexicon is not suited to handling so-called compounding languages like German, Dutch,...

In this paper, we investigate both generative and statistical approaches for language modeling in spoken dialogue systems. Semantic class-based finite state ¢ and-gram grammars are used for improving coverage and modeling accuracy when little training data is available. We have implemented dialogue-state specific language model adaptation to reduce perplexity and improve the efficiency of grammars for spoken dialogue systems. A novel algorithm for state-independent ¢ combining-gram and state-dependent finite state grammars using acoustic confidence scores is proposed. Using this combination strategy, a relative word error reduction of 12 % is achieved for certain dialogue states within a travel reservation task. Finally, semantic class multigrams are proposed and briefly evaluated for language modeling in dialogue systems. 1.

Phrasal segmentation models define a mapping from the words of a sentence to sequences of translatable phrases. We discuss the estimation of these models from large quantities of monolingual training text and describe their realization as weighted finite state transducers for incorporation into phrase-based statistical machine translation systems. Results are reported on the NIST Arabic-English translation tasks showing significant complementary gains in BLEU score with large 5-gram and 6-gram language models. 1

This paper proposes a new lexicon optimization method to improve recognition rate of large scale spontaneous speech recognition. Occurrence count and length of a word has strong correlation with difficulty of recognizing the word. First, we investigate the relation and make a word correctness probability model. The proposed method optimizes the lexicon by making compound words or phrases step by step based on the word correctness probability model so as to improve the estimated recognition rate of the system. The optimization method is applied to a large scale Japanese spontaneous speech corpus. Experimental results show that the language model using the optimized lexicon improves the recognition rate.

This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration except where specifically indicated in the text. It has not been submitted in whole or in part for a degree at any other university. Some of the work has been published previously in conference proceedings (Blackwood et al., 2008a; Blackwood

Building on earlier work that integrates different factors in language modeling, we view (i) backing off to a shorter history and (ii) class-based generalization as two complementary mechanisms of using a larger equivalence class for prediction when the default equivalence class is too small for reliable estimation. This view entails that the classes in a language model should be learned from rare events only and should be preferably applied to rare events. We construct such a model and show that both training on rare events and preferable application to rare events improve perplexity when compared to a simple direct interpolation of class-based with standard language models. 1