Abstract not found

Building probabilistic models of language is a central task in natural language and speech processing allowing to integrate the syntactic and/or semantic (and recently pragmatic) constraints of the language into the systems. Probabilistic language models are an attractive alternative to the more traditional rule-based systems, such as context free grammars, because of the recent availability of massive amount of text corpora which can be used to e#ciently train the models and because instead of binary grammaticality judgement o#ered by the rule-based systems, likelihood of any sequence of lexical units can be obtained, which is a crucial factor in such tasks as speech recognition. Probabilistic language models also find their application in part-of-speech tagging, machine translation, semantic disambiguation and numerous other fields.

Compared to dictation systems, recognition systems for spontaneous speech still perform rather poorly. An important weakness in these systems is the statistical language model, mainly due to the lack of large amounts of stylistically matching training data and to the occurrence of disfluencies in the recognition input. In this paper we investigate a method for improving the robustness of a spontaneous language model by flexible manipulation of the prediction context when disfluencies occur. In the case of repetitions, we obtained significantly better recognition results on a benchmark Switchboard test set. 1.

In state-of-the-art large vocabulary automatic recognition systems, a large statistical language model is used, typically an N-gram. However in order to estimate this model, a large database of sentences or texts in the same style as the recognition task is needed. For spontaneous speech one doesn’t dispose of such database since it should consist of accurate thus expensive orthographic transcriptions of spoken audio. This paper investigates how readily available large news paper corpora can be used to improve language models for spontaneous speech recognition although both language styles differ considerably. A technique is proposed that does a perplexity based automatic selection of appropriate news paper articles and that subsequently uses these texts in the language model estimation. Recognition experiments on spontaneous broadcast speech in Dutch showed significant improvements using this technique. 1.

Declaration This dissertation is the result of my own work and includes nothing which is the outcome of work done in collaboration. 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 [15, 16, 17]. The length of this thesis including appendices, references, footnotes, tables and equations is approximately 56,000 words and contains 42 figures and 40 tables. i Summary It has been found that using a combination of systems for large vocabulary continuous speech recognition (LVCSR) can outperform the use of a single system. For the combination to yield gains, the individual models must be complementary, i.e. they must make different errors. Previous work in ASR has mainly relied on an ad-hoc approach to finding complementary systems. Multiple systems are built, and those that perform well in combination are selected. The multiple diverse systems can be built in many ways, including the use of different frontends, injecting randomness, altering the model topology or using different training

Au,u,4, speech recognition of real-live broadcast news (BN) data(Hu,;: has become a challenging research topic in recent years. This papersur,#CC4; ou key e#orts tobu:6 a largevocabu:6: continu6: speech recognition system for the heterogenou BN taskwithou induuq uduuq6 complexity andcompu4q,x;:# resou4q,x These key e#orts inclu,CC .