Constructing SSML documents with Automatically Generated Intonation Information in a Combinatory Categorial Grammar Framework

Abstract

As of now, Text-to-Speech (TTS) systems are widely used throughout the full spectrum of our activities, and various natural language processing techniques have been utilized to enhance the performance of such TTS systems. As TTS systems begin to play an important role for communication between human and machine, naturalness is considered the most crucial measure of performance for TTS systems, in addition to correctness. General statistical approaches, though widely adopted, are not appropriate for the phenomena as they assign the same intonation to the same sentence. We analyze various kinds of corpus to extract informative features for intonation generation in a Combinatory Categorial Grammar framework, and express intonation-annotated document using Speech Synthesis Markup Language for target system neutral application.