Article details

Research area
Text to speech

Beijing, China


Xin Lu, Tiejun Zhao, Zhanyi Liu, Muyun Yang

Automatic Detection of Prosody Phrase Boundaries for Text-to-Speech Systems


Automatic acquisition of the prosodic phrase boundary detecting rules from the text and speech corpora has always
been a difficulty for TTS systems. We collected over 5,000 sentences as the corpus, introduced a method based on the
transform-based error-driven learning to get the rules for detecting prosodic phrase boundaries, and then used trees to
organize the rules in the TTS system. For using the transformation-based error-driven learning, we designed a set of
templates especially. Using 1,000 sentences to get rules for the TTS system can reach 92% accuracy in close-test and
73% accuracy in open-test.

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