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In this paper we present our Hidden Markov Model (HMM)-based, Mandarin Chinese Text-to-Speech (TTS) system. Mandarin Chinese or Putonghua, “the common spoken language”, is a tone language where each of the 400 plus base syllables can have up to 5 different lexical tone patterns. Their segmental and supra-segmental information is first modeled by 3 corresponding HMMs, including: (1) spectral envelop and gain; (2) voiced/unvoiced and fundamental frequency; and (3) segment duration. The corresponding HMMs are trained from a read speech database of 1,000 sentences recorded by a female speaker. Specifically, the spectral information is derived from short-time LPC spectral analysis. Among all LPC parameters, Line Spectrum Pair (LSP) has the closest relevance to the natural resonances or the “formants” of a speech sound and it is selected to parameterize the spectral information. Furthermore, the property of clustered LSPs around a spectral peak justify augmenting LSPs with their dynamic counterparts, both in time and frequency, in both HMM modeling and parameter trajectory synthesis. One hundred sentences synthesized by 4 LSP-based systems have been subjectively evaluated with an AB comparison test. The listening test results show that LSP and its dynamic counterpart, both in time and frequency, are preferred for the resultant higher synthesized speech quality.