We describe an automatic process for learning word units in Japanese. Since the Japanese orthography has no spaces delimiting words, the first step in building a Japanese speech recognition system is to define the units that will be recognized. Our method applies a compound-finding algorithm, previously used to find word sequences in English, to learning syllable sequences in Japanese. We report that we were able not only to extract meaningful units, eliminating the need for possibly inconsistent manual segmentation, but also to decrease perplexity using this automatic procedure, which relies on a statistical, not syntactic, measure of relevance. Our algorithm also uncovers the kinds of environments that help the recognizer predict phonological alternations, which are often hidden by morphologically-motivated tok- enization.

In this paper, we present a stochastic language modeling tool which aims at retrieving variable-length phrases (multigrams), assuming bigram dependen- cies between them. The phrase retrieval can be intermixed with a phrase clustering procedure, so that the language data are iteratively structured at both a paradigmatic and a syntagmatic level in a fully integrated way. Perplexity results on ATR travel arrangement data with a bi-multigram model (assuming bigram correlations between the phrases) come very close to the trigram scores with a reduced number of entries in the language model. Also the ability of the class version of the model to merge semantically related phrases into a common class is illus- trated.

JANUS II is a research system to design and test components of speech to speech translation systems as well as a research prototype for such a system. We will focus on two aspects of the system: 1) new features and recognition performance of the speech recognition component JANUS-SR and 2) the end-to-end performance of JANUS II, including a comparison of two machine translation strategies used for JANUS-MT (PHOENIX and GLR*). 1. INTRODUCTION Currently JANUS II components for English, German, Korean, Japanese, and Spanish speech input and translation are under development; though not all language pairs can always be kept at the same performance level, multilinguality is required to ensure generality in the recognition and translation approaches. A multitude of smaller and larger scale research projects contribute to the JANUS II system[1], including robust speech recognition[2], noise modeling[3], speaker and channel adaptation, strategies for porting recognition and translation to new...

JANUS II is a research system to design and test components of speech to speech translation systems as well as a research prototype for such a system. We will focus on two aspects of the system: 1) new features and recognition performance of the speech recognition component JANUS-SR and 2) the end-to-end performance of JANUS II, including a comparison of two machine translation strategies used for JANUS-MT (PHOENIX and GLR*). 1. INTRODUCTION Currently JANUS II components for English, German, Korean, Japanese, and Spanish speech input and translation are under development; though not all language pairs can always be kept at the same performance level, multilinguality is required to ensure generality in the recognition and translation approaches. A multitude of smaller and larger scale research projects contribute to the JANUS II system[1], including robust speech recognition[2], noise modeling[3], speaker and channel adaptation, strategies for porting recognition and translation to new...

JANUS II is a research system to design and test components of speech to speech translation systems as well as a research prototype for such a system. We will focus on two aspects of the system: 1) new features and recognition performance of the speech recognition component JANUS-SR and 2) the end-to-end performance of JANUS II, including a comparison of two machine translation strategies used for JANUS-MT (PHOENIX and GLR*). 1. INTRODUCTION Currently JANUS II components for English, German, Korean, Japanese, and Spanish speech input and translation are under development; though not all language pairs can always be kept at the same performance level, multilinguality is required to ensure generality in the recognition and translation approaches. A multitude of smaller and larger scale research projects contribute to the JANUS II system[1], including robust speech recognition[2], noise modeling[3], speaker and channel adaptation, strategies for porting recognition and translation to ne...

In this paper, we describe and evaluate a language model using word classes automatically generated from a word clustering algorithm. Class based language models have been shown to be effective for rapid adaptation, training on small datasets, and reduced memory usage. In terms of model perplexity, prior work has shown diminished returns for class based language models constructed using very large training sets. This paper describes a method of using a class model as a backoff to a bigram model which produced significant benefits even when trained from a large text corpus. Tests results on the Whisper continuous speech recognition system show that for a given word error rate, the clustered bigram model uses 2/3 fewer parameters compared to a standard bigram model using unigram backoff.

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We implement variable n-grams using a word-tree data structure, where nodes represent sequences of words ("contexts") and store how often that context appeared in a training corpus. We build the tree by growing it from the root up. Unlike other methods, there is no pruning step. Instead, we used the simple heuristic of maintaining a priority-queue of candidate leaves, sorted by how often those contexts occurred in the training text. The most popular leaves are then added to the tree, and this process repeats until a specified memory limit is reached. In this way, the tree was able to make branches for longer sentence fragments like "across the street from the" while saving the space from storing uncommon ones.

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.

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