One of the enduring problems in developing high-quality Chinese TTS (text-to-speech) systems is accurate text-to-Pinyin conversion.To solve the problem, identification of words and assignment of correct POS (Part-of-Speech) tags for an input sentence are very important tasks. Also, determining the correct Pinyin of polyphonic characters in unknown words is an important problem in a Chinese TTS system. The unknown word problem has significant effects on the accuracy of the synthesized sound, so accurately predicting the category of unknown words can help a Chinese TTS system to pronounce more naturally. In this paper, we present an SVM(support vector machine)-based method that predicts the unknown words for the results of Chinese word segmentation and POS tagging. For high speed SVM processing to be used in TTS, we pre-detect the candidate boundary of the unknown words before starting the actual prediction. Results of the experiments are very promising by showing high precision and high recall with also very high speed.