Abstract
We explore morphology-based and sub-word language modeling approaches proposed for morphologically rich languages, and evaluate and contrast them for Turkish broadcast news transcription task. In addition, as a morphology-based model, we improve our previously proposed morphology-integrated model for automatic speech recognition. This model is built by composing the finite-state transducer of the morphological parser with a language model over lexical morphemes. This approach provides a morphology-integrated search network with an unlimited vocabulary, generating only valid word forms while reducing the out-of-vocabulary rate and hence improving the word error rate. We also analyze the effect of morpho-tactics and morphological disambiguation on the speech recognition accuracy for the morphology-integrated model. The improved morphology-integrated model performs better than statistically derived sub-word models with added benefit of generating morpho-syntactic and semantic features.
Keywords
Computer scienceMorphemeNatural language processingWord error rateVocabularyArtificial intelligenceLanguage modelParsingTurkishWord (group theory)Agglutinative languageSpeech recognitionLinguistics
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Publication Info
- Year
- 2010
- Type
- article
- Pages
- 5402-5405
- Citations
- 40
- Access
- Closed
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Cite This
Haşim Sak,
Murat Saraçlar,
Tunga Güngör
(2010).
Morphology-based and sub-word language modeling for Turkish speech recognition.
, 5402-5405.
https://doi.org/10.1109/icassp.2010.5494927
Identifiers
- DOI
- 10.1109/icassp.2010.5494927