Automatic Pitch Accent Prediction for Text-To-Speech Synthesis

Ian Read, Stephen J. Cox

Research output: Contribution to conferencePaper

4 Citations (Scopus)

Abstract

Determining pitch accents in a sentence is a key task for a text-to-speech (TTS) system. We describe some methods for pitch accent assignment which make use of features that contain information about a complete phrase or sentence, in contrast to most previous work which has focused on using features local to a syllable or word. Pitch accent prediction is performed using three different techniques: N-gram models of syllable sequences, dynamic programming to match sequences of features, and decision trees. Using a C4.5 decision tree trained on a wide range of features, most notably each word's orthographic form and information extracted from the syntactic parse of the sentence, our feature set achieved a balanced error rate of 46.6%. This compares with the feature set used in [11] which had a balanced error rate of 55.55%.
Original languageEnglish
Pages482-485
Number of pages4
Publication statusPublished - 2007
Event8th Annual Conference of the International Speech Communication Association (INTERSPEECH-2007) - Antwerp, Belgium
Duration: 27 Aug 200731 Aug 2007

Conference

Conference8th Annual Conference of the International Speech Communication Association (INTERSPEECH-2007)
CountryBelgium
CityAntwerp
Period27/08/0731/08/07

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