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A Tree-Trellis N-best Decoder for Stochastic Context-Free Grammars
KTH, Superseded Departments, Speech, Music and Hearing.
2000 (English)In: Proceedings of the International Conference on Spoken Language Processing, Beijing, China, 2000: vol 4, 2000, 282-285 p.Conference paper (Other academic)
Abstract [en]

In this paper a decoder for continuous speech recognition using stochastic context-free grammars is described. It forms the backbone of the ACE recognizer, which is a modular system for real-time speech recognition. A new rationale for automata is introduced, as well as a new model for pruning the search space.

Place, publisher, year, edition, pages
2000. 282-285 p.
National Category
Computer and Information Science
URN: urn:nbn:se:kth:diva-24049OAI: diva2:342999
QC 20100811Available from: 2010-08-11 Created: 2010-08-11 Last updated: 2010-08-12Bibliographically approved
In thesis
1. Efficient Methods for Automatic Speech Recognition
Open this publication in new window or tab >>Efficient Methods for Automatic Speech Recognition
2003 (English)Doctoral thesis, comprehensive summary (Other scientific)
Abstract [en]

This thesis presents work in the area of automatic speech recognition (ASR). The thesis focuses on methods for increasing the efficiency of speech recognition systems and on techniques for efficient representation of different types of knowledge in the decoding process. In this work, several decoding algorithms and recognition systems have been developed, aimed at various recognition tasks.

The thesis presents the KTH large vocabulary speech recognition system. The system was developed for online (live) recognition with large vocabularies and complex language models. The system utilizes weighted transducer theory for efficient representation of different knowledge sources, with the purpose of optimizing the recognition process.

A search algorithm for efficient processing of hidden Markov models (HMMs) is presented. The algorithm is an alternative to the classical Viterbi algorithm for fast computation of shortest paths in HMMs. It is part of a larger decoding strategy aimed at reducing the overall computational complexity in ASR. In this approach, all HMM computations are completely decoupled from the rest of the decoding process. This enables the use of larger vocabularies and more complex language models without an increase of HMM-related computations.

Ace is another speech recognition system developed within this work. It is a platform aimed at facilitating the development of speech recognizers and new decoding methods.

A real-time system for low-latency online speech transcription is also presented. The system was developed within a project with the goal of improving the possibilities for hard-of-hearing people to use conventional telephony by providing speech-synchronized multimodal feedback. This work addresses several additional requirements implied by this special recognition task.

Place, publisher, year, edition, pages
Stockholm: KTH, 2003. iii, 65 p.
Trita-TMH, ISSN 1104-5787 ; 2003:14
speech recognition, algorithms, hidden markov models, HMM, weigted finite-state transducers
urn:nbn:se:kth:diva-3675 (URN)91-7283-657-1 (ISBN)
Public defence
2003-12-17, 00:00
QC 20100811Available from: 2003-12-11 Created: 2003-12-11 Last updated: 2010-08-12Bibliographically approved

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Seward, Alexander
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