In this paper, we present novel techniques for performing topic adaptation on an¢-gram language model. Given training text labeled with topic information, we automatically identify the most relevant topics for new text. We adapt our language model toward these topics using an exponential model, by adjusting probabilities in our model to agree with those found in the topical subset of the training data. For efficiency, we do not normalize the model; that is, we do not require that the “probabilities” in the language model sum to 1. With these techniques, we were able to achieve a modest reduction in speech recognition word-error rate in the Broadcast News domain.

Adapting language models across styles and topics, such as for lecture transcription, involves combining generic style models with topic-specific content relevant to the target document. In this work, we investigate the use of the Hidden Markov Model with Latent Dirichlet Allocation (HMM-LDA) to obtain syntactic state and semantic topic assignments to word instances in the training corpus. From these context-dependent labels, we construct style and topic models that better model the target document, and extend the traditional bag-of-words topic models to n-grams. Experiments with static model interpolation yielded a perplexity and relative word error rate (WER) reduction of 7.1 % and 2.1%, respectively, over an adapted trigram baseline. Adaptive interpolation of mixture components further reduced perplexity by 9.5 % and WER by a modest 0.3%. 1

Traditionally, indexing and searching of speech content in multimedia databases have been achieved through a combination of separately constructed speech recognition and information retrieval engines. Although each technology has a legacy of research, only recently have efforts been made to study the potential suboptimality of this strategy, and none of these efforts specifically addresses the presence of uncertainty in automatically generated transcriptions. This research develops a refinement of the most common information retrieval relevance formula, TFIDF, to incorporate uncertainty as a retrieval feature, along with a set of techniques to acquire this uncertainty from multiple hypotheses produced by existing speech recognition data structures. In the process a greater amount of evidence is extracted than is available in the most likely transcription hypothesis, and overall retrieval precision and recall are improved. The term weighting scheme known as the inverse document frequenc...

In the past several years, a number of different language modeling improvements over simple trigram models have been found, including caching, higher-order n-grams, skipping, modified Kneser-Ney smoothing, and clustering. While all of these techniques have been studied separately, they have rarely been studied in combination. We find some significant interactions, especially with smoothing techniques. The combination of all techniques leads to up to a 45% perplexity reduction over a Katz smoothed trigram model with no count cuto#s, the highest such perplexity reduction reported.

this document. The first section, in chapter 3, develops a model for syntactic dependencies based on word-category n-grams. The second section, in chapter 4, extends this model by allowing short-range word relations to be captured through the incorporation of selected word n-grams.

The subject matter of any conversation or document can typically be described as some combination of elemental topics. We have developed a language model adaptation scheme that takes a piece of text, chooses the most similar topic clusters from a set of over 5000 elemental topics, and uses topic specific language models built from the topic clusters to rescore N-best lists. We are able to achieve a 15% reduction in perplexity and a small improvement in word error rate by using this adaptation. We also investigate the use of a topic tree, where the amount of training data for a specific topic can be judiciously increased in cases where the elemental topic cluster has too few word tokens to build a reliably smoothed and representative language model. Our system is able to fine-tune topic adaptation by interpolating models chosen from thousands of topics, allowing for adaptation to unique, previously unseen combinations of subjects.

Topic adaptation for language modeling is concerned with adjusting the probabilities in a language model to better reflect the expected frequencies of topical words for a new document. The language model to be adapted is usually built from large amounts of training text and is considered representative of the current domain. In order to adapt this model for a new document, the topic (or topics) of the new document are identified. Then, the probabilities of words that are more likely to occur in the identified topic(s) than in general are boosted, and the probabilities of words that are unlikely for the identified topic(s) are suppressed. We present a novel technique for adapting a languagemodel to the topic of a document, using a nonlinear interpolation of n-gram language models. A three-way, mutually exclusive division of the vocabulary into general, on-topic and off-topic word classes is used to combine word predictions from a topic-specific and a general language model. We achieve ...

This paper describes the use of a weighted mixture of classbased n-gram language models to perform topic adaptation. By using a fixed class n-gram history and variable word-given-class probabilities we obtain large improvements in the performance of the class-based language model, giving it similar accuracy to a word n-gram model, and an associated small but statistically significant improvement when we interpolate with a word-based n-gram language model. 1 Introduction Previously, weighted mixtures of word n-gram language models have been used to provide a topic adaptation method for large vocabulary speech recognition [3]. A disadvantage of this method, however, is that these models require large numbers of parameters per topic, which in turn necessitates a large quantity of training data for each topic and associated storage space per topic in the resulting language model. We investigate an alternative approach to topic mixtures within the n-gram paradigm by using a class-based n-...

Accurate statistical language models are needed, for example, for large vocabulary speech recognition. The construction of models that are computationally efficient and able to utilize long-term dependencies in the data is a challenging task. In this article we describe how a topical clustering obtained by ordered maps of document collections can be utilized for the construction of efficiently focusing statistical language models. Experiments on Finnish and English texts demonstrate that considerable improvements are obtained in perplexity compared to a general n-gram model and to manually classified topic categories. In the speech recognition task the recognition history and the current hypothesis can be utilized to focus the model towards the current discourse or topic, and then apply the focused model to re-rank the hypothesis.

Standard statistical language models, or n-gram models, which represent the probability of word sequences, suffer from sparse-data problems in tasks where large amounts of domain-specific text are not available. This thesis focuses on improving the estimation of domain-dependent n-gram models by using out-of-domain text data. Previous approaches for estimating language models from multi-domain data have not accounted for the characteristic variations of style and content across domains. In contrast, this thesis introduces two approaches that compensate for multi-domain differences, both representing "style" by part-of-speech (POS) sequences and "content" by the particular choice of words. First, data from multiple domains is combined using similarity weighting schemes that discriminate for content and style relevance prior to pooling multi-domain text. Second, n-gram distributions from multiple domains are combined, via a POS-dependent n-gram framework that separately compensate for word and POS usage differences. Two variations are explored: explicitly transforming the out-of-domain distribution before combining with an in-domain model, and vi separately estimating components of the POS-dependent n-gram model using multidomain data. Finally, measures to analyze and predict recognition performance of language models are also investigated, resulting in an algorithm for predicting performance differences associated with localized changes in language models given a recognition system.