Advanced search options

Advanced Search Options 🞨

Browse by author name (“Author name starts with…”).

Find ETDs with:

in
/  
in
/  
in
/  
in

Written in Published in Earliest date Latest date

Sorted by

Results per page:

You searched for subject:(sanapala). One record found.

Search Limiters

Last 2 Years | English Only

No search limiters apply to these results.

▼ Search Limiters

1. Siivola, Vesa. Language Models for Automatic Speech Recognition: Construction and Complexity Control.

Degree: 2007, Helsinki University of Technology

The language model is one of the key components of a large vocabulary continuous speech recognition system. Huge text corpora can be used for training the language models. In this thesis, methods for extracting the essential information from the training data and expressing the information as a compact model are studied. The thesis is divided in three main parts. In the first part, the issue of choosing the best base modeling unit for the prevalent language modeling method, n-gram language modeling, is examined. The experiments are focused on morpheme-like subword units, although syllables are also tried. Rule-based grammatical methods and unsupervised statistical methods for finding morphemes are compared with the baseline word model. The Finnish cross-entropy and speech recognition experiments show that significantly more efficient models can be created using automatically induced morpheme-like subword units as the basis of the language model. In the second part, methods for choosing the n-grams that have explicit probability estimates in the n-gram model are studied. Two new methods specialized on selecting the n-grams for Kneser-Ney smoothed n-gram models are presented, one for pruning and one for growing the model. The methods are compared with entropy-based pruning and Kneser pruning. Experiments on Finnish and English text corpora show that the proposed pruning method gives considerable improvements over the previous pruning algorithms for Kneser-Ney smoothed models and also is better than entropy pruned Good-Turing smoothed model. Using the growing algorithm for creating a starting point for the pruning algorithm further improves the results. The improvements in Finnish speech recognition over the other Kneser-Ney smoothed models were significant as well. To extract more information from the training corpus, words should not be treated as independent tokens. The syntactic and semantic similarities of the words should be taken into account in the language model. The last part of this thesis explores, how these similarities can be modeled by mapping the words into continuous space representations. A language model formulated in the state-space modeling framework is presented. Theoretically, the state-space language model has several desirable properties. The state dimension should determine, how much the model is forced to generalize. The need to learn long-term dependencies should be automatically balanced with the need to remember the short-term dependencies in detail. The experiments show that training a model that fulfills all the theoretical promises is hard: the training algorithm has high computational complexity and it mainly finds local minima. These problems still need further research.

Kielimalli on yksi avainosa suurisanastoisessa jatkuvan puheen tunnistusjärjestelmässä. Valtavia tekstiaineistoja on saatavilla kielimallien opettamiseen. Tässä väitöstyössä tutkitaan, miten opetusaineistosta löydetään oleelliset asiat ja miten ne voidaan esittää tiiviisti mallissa. Väitöstyö on jaettu kolmeen…

Advisors/Committee Members: Helsinki University of Technology, Department of Computer Science and Engineering, Laboratory of Computer and Information Science.

Subjects/Keywords: language model; speech recognition; subword unit; morpheme segmentation; variable order n-gram model; pruning; growing; state-space language model; kielimalli; puheentunnistus; sanapala; morfeemeihin jako; vaihtelevanasteinen n-grammimalli; karsiminen; kasvattaminen; tila-avaruuskielimalli

Record DetailsSimilar RecordsGoogle PlusoneFacebookTwitterCiteULikeMendeleyreddit

APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Siivola, V. (2007). Language Models for Automatic Speech Recognition: Construction and Complexity Control. (Thesis). Helsinki University of Technology. Retrieved from http://lib.tkk.fi/Diss/2007/isbn9789512288946/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Chicago Manual of Style (16th Edition):

Siivola, Vesa. “Language Models for Automatic Speech Recognition: Construction and Complexity Control.” 2007. Thesis, Helsinki University of Technology. Accessed November 18, 2019. http://lib.tkk.fi/Diss/2007/isbn9789512288946/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

MLA Handbook (7th Edition):

Siivola, Vesa. “Language Models for Automatic Speech Recognition: Construction and Complexity Control.” 2007. Web. 18 Nov 2019.

Vancouver:

Siivola V. Language Models for Automatic Speech Recognition: Construction and Complexity Control. [Internet] [Thesis]. Helsinki University of Technology; 2007. [cited 2019 Nov 18]. Available from: http://lib.tkk.fi/Diss/2007/isbn9789512288946/.

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

Council of Science Editors:

Siivola V. Language Models for Automatic Speech Recognition: Construction and Complexity Control. [Thesis]. Helsinki University of Technology; 2007. Available from: http://lib.tkk.fi/Diss/2007/isbn9789512288946/

Note: this citation may be lacking information needed for this citation format:
Not specified: Masters Thesis or Doctoral Dissertation

.