Unsupervised Continuous-Valued Word Features for Phrase-Break Prediction without a Part-of-Speech Tagger
نویسندگان
چکیده
Part of speech (POS) tags are foremost among the features conventionally used to predict intonational phrase-breaks for text to speech (TTS) conversion. The construction of such systems therefore presupposes the availability of a POS tagger for the relevant language, or of a corpus manually tagged with POS. However, such tools and resources are not available in the majority of the world’s languages, and manually labelling text with POS tags is an expensive and time-consuming process. We therefore propose the use of continuous-valued features that summarise the distributional characteristics of word types as surrogates for POS features. Importantly, such features are obtained in an unsupervised manner from an untagged text corpus. We present results on the phrase-break prediction task, where use of the features closes the gap in performance between a baseline system (using only basic punctuation-related features) and a topline system (incorporating a state-of-the-art POS tagger).
منابع مشابه
Learning continuous-valued word representations for phrase break prediction
Phrase break prediction is the first step in modeling prosody for text-to-speech systems (TTS). Traditional methods of phrase break prediction have used discrete linguistic representations (like POS tags, induced POS tags, word-terminal syllables) for modeling these breaks. However these discrete representations suffer from a number of issues such as fixing the number of discrete classes and al...
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