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In this paper we describe a word clustering method for class-based n-gram model. The measurement for clustering is the entropy on a corpus different from the corpus for n-gram model estimation. The search method is based on the greedy algorithm. We applied this method to a Japanese EDR corpus and English Penn Treebank corpus. The perplexities of word-based(More)
This paper introduces a discriminative training for language models (LMs) by leveraging phoneme similarities estimated from an acoustic model. To train an LM discriminatively, we needed the correct word sequences and the recognized results that Automatic Speech Recognition (ASR) produced by processing the utterances of those correct word sequences. But,(More)
We applied mobile computing to community support and explored mobile computing with a large number of terminals. This article reports on the Second International Conference on Multiagent Systems (ICMAS'96) Mobile Assistant Project that was conducted at an actual international conference for multiagent systems using 100 personal digital assistants (PDAs) and(More)
This paper presents a strategy for efficiently selecting informative data from large corpora of untranscribed speech. Confidence-based selection methods (i.e., selecting utterances we are least confident about) have been a popular approach, though they only look at the top hypothesis when selecting utterances and tend to select outliers, therefore, not(More)
This paper introduces a method to train an error-corrective model for Automatic Speech Recognition (ASR) without using audio data. In existing techniques, it is assumed that sufficient audio data of the target application is available and negative samples can be prepared by having ASR recognize this audio data. However, this assumption is not always true.(More)
Named Entity (NE) recognition from the results of Automatic Speech Recognition (ASR) is challenging because of ASR errors. To detect NEs, one of the options is to use a statistical NE model that is usually trained with ASR one-best results. In order to make NE recognition more robust to ASR errors, we propose using Word Confusion Networks (WCNs), sequences(More)
]in this paper, we present a stochastic language model using dependency. This model considers a sentence as a word sequence and predicts each word from left to right. The history at each step of prediction is a sequence of partial parse krees covering the preceding words. First ore: model predicts the partial parse trees which have a dependency relation(More)