CiteULike: lschilli's library [33 articles]

Corpus-Based Training of Action-Specific Language Models

Lars Schillingmann — 2007-07-13T10:06:54-00:00

(2007)

The impact of spoken interfaces on the design of service robots

G Bugmann — 2005-11-25T17:03:59-00:00

Industrial Robot: An International Journal, Vol. 32, No. 6. (January 2005), pp. 499-504.

Die Evaluierung von Spracherkennungssystemen in Deutschland

Erwin Paulus — 2007-03-14T20:34:33-00:00

(July 1995)

Principal Component Analysis

IT Jolliffe — 2007-03-11T15:04:52-00:00

(01 October 2002)

Principal component analysis is central to the study of multivariate data. Although one of the earliest multivariate techniques it continues to be the subject of much research, ranging from new model- based approaches to algorithmic ideas from neural networks. It is extremely versatile with applications in many disciplines. The first edition of this book was the first comprehensive text written solely on principal component analysis. The second edition updates and substantially expands the original version, and is once again the definitive text on the subject. It includes core material, current research and a wide range of applications. Its length is nearly double that of the first edition. Researchers in statistics, or in other fields that use principal component analysis, will find that the book gives an authoritative yet accessible account of the subject. It is also a valuable resource for graduate courses in multivariate analysis. The book requires some knowledge of matrix algebra. Ian Jolliffe is Professor of Statistics at the University of Aberdeen. He is author or co-author of over 60 research papers and three other books. His research interests are broad, but aspects of principal component analysis have fascinated him and kept him busy for over 30 years.

Introduction To Algorithms

Thomas Cormen — 2007-03-09T13:48:08-00:00

(1998)

Taschenbuch der Mathematik

Ilja Bronstein — 2007-03-06T12:51:13-00:00

(September 2000)

Modelling the Effects of Speech Rate Variation for Automatic Speech Recognition

B Wrede — 2007-03-03T17:41:17-00:00

(2002)

Handbuch zur Datenaufnahme und Transliteration in TP14 von VERBMOBIL -- 3.0

Klaus Kohler — 2007-03-03T17:19:30-00:00

Vol. VERBMOBIL Technisches Dokument Nr. 11 (September 1994)

On the Use of Information Retrieval Measures for Speech Recognition Evaluation

I Mccowan — 2007-03-01T01:43:20-00:00

No. 73. (2004)

This paper discusses the evaluation of automatic speech recognition (ASR) systems developed for practical applications, suggesting a set of criteria for application-oriented performance measures. The commonly used word error rate (WER), which poses ASR evaluation as a string editing process, is shown to have a number of limitations with respect to these criteria, motivating alternative or additional measures. This paper suggests that posing speech recognition evaluation as an information retrieval problem, where each word is one unit of information, offers a flexible framework for application-oriented performance analysis based on the concepts of recall and precision.

A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection

Ron Kohavi — 2006-03-05T17:32:28-00:00

(1995), pp. 1137-1145.

We review accuracy estimation methods and compare the two most common methods: crossvalidation and bootstrap. Recent experimental results on artificial data and theoretical results in restricted settings have shown that for selecting a good classifier from a set of classifiers (model selection), ten-fold cross-validation may be better than the more expensive leaveone -out cross-validation. We report on a largescale experiment---over half a million runs of C4.5 and a Naive-Bayes algorithm---to...

Generation of Language Models Using the Results of Image Analysis

U Naeve — 2007-02-28T15:24:55-00:00

(1995), pp. 1739-1742.

An Object-oriented Approach Using a Top-down and Bottom-up Process for Manipulative Action Recognition

Zhe Li — 2007-02-28T15:24:39-00:00

Vol. 4174 (2006), pp. 212-221.

Maintaining knowledge about temporal intervals

James Allen — 2005-05-10T22:22:20-00:00

Communications of the ACM, Vol. 26, No. 11. (November 1983), pp. 832-843.

Linking Speech and Gesture in Multimodal Instruction Systems

JC Wolf — 2007-02-16T14:11:48-00:00

(6-8 September 2006), pp. 141-144.

Recent advances in the automatic recognition of audiovisual speech

G Potamianos — 2007-02-12T23:55:39-00:00

Proceedings of the IEEE, Vol. 91, No. 9. (2003), pp. 1306-1326.

Visual speech information from the speaker's mouth region has been successfully shown to improve noise robustness of automatic speech recognizers, thus promising to extend their usability in the human computer interface. In this paper, we review the main components of audiovisual automatic speech recognition (ASR) and present novel contributions in two main areas: first, the visual front-end design, based on a cascade of linear image transforms of an appropriate video region of interest, and subsequently, audiovisual speech integration. On the latter topic, we discuss new work on feature and decision fusion combination, the modeling of audiovisual speech asynchrony, and incorporating modality reliability estimates to the bimodal recognition process. We also briefly touch upon the issue of audiovisual adaptation. We apply our algorithms to three multisubject bimodal databases, ranging from small- to large-vocabulary recognition tasks, recorded in both visually controlled and challenging environments. Our experiments demonstrate that the visual modality improves ASR over all conditions and data considered, though less so for visually challenging environments and large vocabulary tasks.

Hearing lips and seeing voices

Harry Mcgurk — 2007-02-11T20:51:50-00:00

Nature, Vol. 264, No. 5588. (23 December 1976), pp. 746-748.

Automatische Spracherkennung: Grundlagen, statistische Modelle und effiziente Algorithmen

Ernst Schukat-Talamazzini — 2007-01-13T15:16:29-00:00

(1995)

Mustererkennung mit Markov-Modellen

Gernot Fink — 2007-01-09T17:14:19-00:00

(31 October 2003)

Statistical Methods for Speech Recognition

Frederick Jelinek — 2005-08-31T14:41:48-00:00

(16 January 1998)

This book reflects decades of important research on the mathematical foundations of speech recognition. It focuses on underlying statistical techniques such as hidden Markov models, decision trees, the expectation-maximization algorithm, information theoretic goodness criteria, maximum entropy probability estimation, parameter and data clustering, and smoothing of probability distributions. The author's goal is to present these principles clearly in the simplest setting, to show the advantages of self-organization from real data, and to enable the reader to apply the techniques.

Spoken Language Processing: A Guide to Theory, Algorithm and System Development

Xuedong Huang — 2005-07-22T23:53:57-00:00

(25 April 2001)

Incremental Speech Recognition for Multimodal Interfaces

GA Fink — 2006-12-19T16:15:34-00:00

(September 1998), pp. 2012-2017.

Robust Audiovisual Integration using Semicontinuous Hidden Markov Models

Q Su — 2006-12-19T01:06:43-00:00

Vol. 1 (1996), pp. 42-45.

We describe an improved method of integrating audio and visual information in a HMM-based audiovisual ASR system. The method uses a modified semi-continuous HMM (SCHMM) for integration and recognition. Our results show substantial improvements over earlier integration methods at high noise levels.

Improving Speech Recognition on a Mobile Robot Platform through the use of Top-Down Visual Queues

RJ Ross — 2006-12-19T00:38:12-00:00

(9-15 August 2003)

Audio-Visual Large Vocabulary Continuous Speech Recognition in the Broadcast Domain

S Basu — 2006-12-19T00:08:26-00:00

(1999)

We consider the problem of combining visual cues with audio signals for the purpose of improved automatic machine recognition of speech. Although signifcant progress has been made in machine transcription of large vocabulary continuous speech (LVCSR) over the last few years, the technology to date is most eective only under controlled conditions such as low noise, speaker dependent recognition and read speech (as opposed to conversational speech) etc. On the otherhand, while augmenting the...

Using the Visual Component in Automatic Speech Recognition

NM Brooke — 2006-12-18T20:17:37-00:00

Vol. 3 (1996), pp. 1656-1659.

The movements of talkers' faces are known to convey visual cues that can improve speech intelligibility, especially where there is noise or hearing-impairment. This suggests that visible facial gestures could be exploited to enhance speech intelligibility in automatic systems. Handling the volume of data represented by images of talkers' faces implies some form of data compression. Rather than using conventional feature extraction approaches, image coding and compression can be achieved using...

Audio-Visual Speech Recognition, Final Workshop 2000 Report

C Neti — 2006-12-18T18:27:13-00:00

(12 October 2000)

Automatic Detection of Song Changes in Music Mixes Using Stochastic Models

Thomas Plötz — 2006-09-15T14:05:10-00:00

Vol. 3 (2006), pp. 665-668.

The annotation of song changes in music mixes created by DJs or radio stations for direct access in digital recordings is, usually, a very tedious work. In order to support this process we developed an automatic song change detection method which can be used for arbitrary music mixes. Stochastic models are applied to music data aiming at their segmentation with respect to automatically obtained abstract generic acoustic units. The local analysis of these stochastic music models provides hypotheses for song changes. Results of an experimental evaluation processing music mix data demonstrate the effectiveness of our method for supporting the annotation with respect to song changes.

Lexicalized grammar acquisition

M Ninomiya — 2006-09-28T12:04:23-00:00

(2003)

This paper presents a formalization of automatic grammar acquisition that is based on lexicalized grammar formalisms (e.g. LTAG and HPSG). We state the conditions for the consistent ac- quisition of a unique lexicalized grammar from an annotated corpus.

Towards automatic grammar acquisition from a bracketed corpus

Thanaruk Theeramunkong — 2006-09-28T10:25:59-00:00

(1996)

In this paper, we propose a method to group brackets in a bracketed corpus (with lexical tags), according to their local contextual information, as a first step towards the automatic acquisition of a context-free grammar. Using a bracketed corpus, the learning task is reduced to the problem of how to determine the nonterminal label of each bracket in the corpus. In a grouping process, a single nonterminal label is assigned to each group of brackets which are similar. Two techniques,...

Zeitsynchrone Suche mit n-Gramm-Modellen höherer Ordnung

GA Fink — 2006-09-27T23:33:56-00:00

(2000), pp. 145-150.

Learning visually grounded words and syntax of natural spoken language

Deb Roy — 2006-09-27T15:06:54-00:00

Evolution of Communication, Vol. 4, No. 1. (2001), pp. 33-56.

Learning visually-grounded words and syntax for a scene description task

D Roy — 2006-09-27T14:08:28-00:00

Computer Speech and Language (2002)

A spoken language generation system has been developed that learns to describe objects in computer-generated visual scenes. The system is trained by a `show-and-tell' procedure in which visual scenes are paired with natural language descriptions. Learning algorithms acquire probabilistic structures which encode the visual semantics of phrase structure, word classes, and individual words. Using these structures, a planning algorithm integrates syntactic, semantic, and contextual constraints to...

Towards situated speech understanding: visual context priming of language models

Deb Roy — 2006-09-27T13:55:18-00:00

Computer Speech & Language, Vol. 19, No. 2. (April 2005), pp. 227-248.

Fuse is a situated spoken language understanding system that uses visual context to steer the interpretation of speech. Given a visual scene and a spoken description, the system finds the object in the scene that best fits the meaning of the description. To solve this task, Fuse performs speech recognition and visually-grounded language understanding. Rather than treat these two problems separately, knowledge of the visual semantics of language and the specific contents of the visual scene are fused during speech processing. As a result, the system anticipates various ways a person might describe any object in the scene, and uses these predictions to bias the speech recognizer towards likely sequences of words. A dynamic visual attention mechanism is used to focus processing on likely objects within the scene as spoken utterances are processed. Visual attention and language prediction reinforce one another and converge on interpretations of incoming speech signals which are most consistent with visual context. In evaluations, the introduction of visual context into the speech recognition process results in significantly improved speech recognition and understanding accuracy. The underlying principles of this model may be applied to a wide range of speech understanding problems including mobile and assistive technologies in which contextual information can be sensed and semantically interpreted to bias processing.