A Framework for Language-Independent Analysis and Prosodic Feature Annotation of Text Corpora
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- Spiliotopoulos D., Petasis G., Kouroupetroglou G. (2008) A Framework for Language-Independent Analysis and Prosodic Feature Annotation of Text Corpora. In: Sojka P., Horák A., Kopeček I., Pala K. (eds) Text, Speech and Dialogue. TSD 2008. Lecture Notes in Computer Science, vol 5246. Springer, Berlin, Heidelberg
Concept-to-Speech systems include Natural Language Generators that produce linguistically enriched text descriptions which can lead to significantly improved quality of speech synthesis. There are cases, however, where either the generator modules produce pieces of non-analyzed, non-annotated plain text, or such modules are not available at all. Moreover, the language analysis is restricted by the usually limited domain coverage of the generator due to its embedded grammar. This work reports on a language-independent framework basis, linguistic resources and language analysis procedures (word/sentence identification, part-of-speech, prosodic feature annotation) for text annotation/processing for plain or enriched text corpora. It aims to produce an automated XML- annotated enriched prosodic markup for English and Greek texts, for improved synthetic speech. The markup includes information for both training the synthesizer and for actual input for synthesising. Depending on the domain and target, different methods may be used for automatic classification of entities (words, phrases, sentences) to one or more preset categories such as “emphatic event”, “new/old information”, “second argument to verb”, “proper noun phrase”, etc. The prosodic features are classified according to the analysis of the speech-specific characteristics for their role in prosody modelling and passed through to the synthesizer via an extended SOLE-ML description. Evaluation results show that using selectable hybrid methods for part-of-speech tagging high accuracy is achieved. Annotation of a large generated text corpus containing 50% enriched text and 50% canned plain text produces a fully annotated uniform SOLE-ML output containing all prosodic features found in the initial enriched source. Furthermore, additional automatically-derived prosodic feature annotation and speech synthesis related values are assigned, such as word-placement in sentences and phrases, previous and next word entity relations, emphatic phrases containing proper nouns, and more.
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