Arşiv ve Dokümantasyon Merkezi
Dijital Arşivi

Integrating morphology into automatic speech recognition: morpholexical and discriminative language models for Turkish

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dc.contributor Ph.D. Program in Computer Engineering.
dc.contributor.advisor Güngör, Tunga.
dc.contributor.advisor Saraçlar, Murat.
dc.contributor.author Sak, Haşim.
dc.date.accessioned 2023-03-16T10:13:35Z
dc.date.available 2023-03-16T10:13:35Z
dc.date.issued 2011.
dc.identifier.other CMPE 2011 H25 PhD
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/12573
dc.description.abstract Languages with agglutinative or in ectional morphology have proven to be challenging for speech and language processing due to relatively large vocabulary sizes leading to a high number of out-of-vocabulary (OOV) words. In this thesis, we tackle with these challenges in automatic speech recognition (ASR) for Turkish which has an extremely productive in ectional and derivational morphology. First, we build the necessary tools and resources for Turkish, namely a nite-state morphological parser, a perceptron-based morphological disambiguator, and a text corpus collected from the world wide web. Second, we introduce two complementary language modeling approaches to alleviate the OOV word problem and to exploit morphology as a knowledge source. The first, morpholexical language model, is a generative n-gram model, where modeling units are lexical-grammatical morphemes instead of commonly used words or statistical sub-words. The second is a linear reranking model trained discriminatively with a variant of the perceptron algorithm, word error rate (WER) sensitive perceptron, using morpholexical and morphosyntactic features to rerank n-best candidates obtained with the generative model. We apply the proposed models in Turkish broadcast news transcription task and give experimental results. We also propose a novel approach for integrating morphology into an ASR system in the nite-state transducer framework as a knowledge source. The morpholexical model is highly e ective in alleviating the OOV problem and improves the WER over word and statistical sub-word models by 1.8% and 0.8% absolute, respectively. The discriminatively trained model further improves the WER of the system by 0.8% absolute. Finally, we present an algorithm for on-the-fly lattice rescoring with low-latency.
dc.format.extent 30 cm.
dc.publisher Thesis (Ph.D.) - Bogazici University. Institute for Graduate Studies in Science and Engineering, 2011.
dc.relation Includes appendices.
dc.relation Includes appendices.
dc.subject.lcsh Language and languages -- Identification -- Data processing.
dc.subject.lcsh Automatic speech recognition.
dc.subject.lcsh Turkish dialects -- Data processing.
dc.title Integrating morphology into automatic speech recognition: morpholexical and discriminative language models for Turkish
dc.format.pages xvii, 125 leaves ;


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