A unified framework for text snalysis in Chinese TTS

This paper presents a robust text analysis system for Chinese text-to-speech synthesis. In this study, a lexicon word or a continuum of non-hanzi characters with the same category (e.g. a digit string) are defined as a morpheme, which is the basic unit forming a Chinese word. Based on this definition, the three key issues concerning the interpretation of real Chinese text, namely lexical disambiguation, unknown word resolution and non-standard word (NSW) normalization can be unified in a single framework and reformulated as a two-pass tagging task on a sequence of morphemes. Our system consists of four main components: (1) a pre-segmenter for sentence segmentation and morpheme segmentation; and (2) a lexicalized HMM-based chunker for identifying unknown words and guessing their part-of-speech categories; and (3) a HMM-based tagger for converting orthographic morphemes to their Chinese phonetic representation (viz. pinyin), given their word-formation patterns and part-of-speech information; (4) a post-processing for interpreting phonetic tags and fine-tuning pronunciation order for some special NSWs if necessary. The evaluation on a pinyin-notated corpus built from the Peking University corpus shows that our system can achieve correct interpretation for most words.