We demonstrate that transformation-based learning can be used to correct noisy speech recognition transcripts in the lecture domain with an average word error rate reduction of 12.9%. Our method is distinguished from earlier related work by its robustness to small amounts of training data, and its resulting efficiency, in spite of its use of true word error rate computations as a rule scoring function. 1

kiyonori.ohtake [at] nict.go.jp This paper discusses sampling strategies for building a dependency-analyzed corpus and analyzes them with different kinds of corpora. We used the Kyoto Text Corpus, a dependency-analyzed corpus of newspaper articles, and prepared the IPAL corpus, a dependency-analyzed corpus of example sentences in dictionaries, as a new and different kind of corpus. The experimental results revealed that the length of the test set controlled the accuracy and that the longest-first strategy was good for an expanding corpus, but this was not the case when constructing a corpus from scratch. 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

Selective generation of training examples in

Abstract—We analyze the problem of call-type classification using data that is weakly labelled. The training data is not systematically annotated, but we consider we have a weak or lazy oracle able to answer the question “Is sample x of class q?” by a simple ‘yes ’ or ‘no ’ answer. This situation of learning might be encountered in many real-world problems where the cost of labelling data is very high. We prove that it is possible to learn linear classifiers in this setting, by estimating adequate expectations inspired by the Multiclass Bandit paradgim. We propose a learning strategy that builds on Kessler’s construction to learn multiclass perceptrons. We test our learning procedure against two real-world datasets from spoken langage understanding and provide compelling results. I.

Abstract: This paper presents a brief survey on Automatic Speech recognition and discusses the major themes and advances made in the past. After years of research and development the accuracy of automatic speech recognition remains one of the important research challenges. The design of speech Recognition system requires careful attentions the following issues: Definition of various types of speech classes, Speech representation, feature extraction techniques, speech classifiers, Database and performance evaluation. The problems that are existing in ASR and the various techniques to solve these problems constructed by various research workers have been presented in a chronological order. The objective of this review paper is to summarize and compare some of the well known methods used in various stages of speech recognition system and identify research topic and applications which are at the forefront of this exciting and challenging field.