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dc.rights.licenseCC-BY-NC-ND
dc.contributor.advisorBloothooft, G.
dc.contributor.advisorBinnenpoorte, D.
dc.contributor.authorVersteegh, B.
dc.date.accessioned2012-09-06T17:03:17Z
dc.date.available2012-09-06
dc.date.available2012-09-06T17:03:17Z
dc.date.issued2012
dc.identifier.urihttps://studenttheses.uu.nl/handle/20.500.12932/20733
dc.description.abstractCurrent Text-to-Speech software such as Vocalizer is able to produce fairly natural speech from texts that do not contain meta-text. Meta-text that is part of most modern electronic text-formats is ignored by Vocalizer, resulting in unnatural output, or loss of structural information. The present research designs and tests a method to preserve meta-text information in Text-to-Speech conversion. Preservation was done by mapping various structural elements in the e-text to speech, non-speech audio and pauses. A listening experiment, using 23 participants, was performed to measure this method's effectiveness in improving three aspects: listening comfort; perceived speech intelligibility and perceived synthesis quality. In the case of list-structures, significant improvements between 18% and 30% were measured in all three aspects. Omission of a large data-table resulted in significant improvements between 21% and 61% in all three aspects as well. Mappings for headings, images, page-breaks did not result in significant improvements.
dc.description.sponsorshipUtrecht University
dc.format.extent217348 bytes
dc.format.mimetypeapplication/pdf
dc.language.isoen_US
dc.titleUsing meta-text to improve intelligibility of speech-synthesized e-texts
dc.type.contentBachelor Thesis
dc.rights.accessrightsOpen Access
dc.subject.keywordstext-to-speech, meta-text, markup, speech-synthesis, sound icons, vocalizer
dc.subject.courseuuKunstmatige Intelligentie


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