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Language modeling plays a critical role for automatic speech recognition. Typically, the n-gram language models suffer from the lack of a good representation of historical words and an inability to estimate unseen parameters due to insufficient training data. In this study, we explore the application of latent semantic information (LSI) to language modeling and parameter smoothing. Our approach adopts latent semantic analysis to transform all words and documents into a common semantic space. The word-to-word, word-to-document and document-to-document relations are, accordingly, exploited for language modeling and smoothing. For language modeling, we present a new representation of historical words based on retrieval of the most relevant document. We also develop a novel parameter smoothing method, where the language models of seen and unseen words are estimated by interpolating the k nearest seen words in the training corpus. The interpolation coefficients are determined according to the closeness of words in the semantic space. As shown by experiments, the proposed modeling and smoothing methods can significantly reduce the perplexity of language models with moderate computational cost.

Language modeling using n-gram is popular for speech recognition and many other applications. The conventional ngram suffers from the insufficiencies of training data, domain knowledge and long distance language dependencies. This paper presents a new approach to mining long distance word associations and incorporating their mutual information into language models. We aim to discover the associations of multiple distant words from training corpus. An efficient algorithm is exploited to merge the frequent word subsets and construct the association patterns. The resulting association pattern n-gram is general with a special realization to trigger pair n-gram where only associations of two distant words are considered. To improve the modeling, we further compensate the weaknesses of sparse training data via parameter smoothing and domain mismatch via online adaptive learning. The proposed association pattern n-gram and several hybrid models are successfully applied for speech recognition. We also find that the incorporation of mutual information of association patterns can significantly reduce the perplexities of language models.