An algorithm for speech parameter generation from continuous mixture HMMs with dynamic features
Keiichi Tokuda, Takashi Masuko, Tetsuya Yamada, Takao Kobayashi, Satoshi Imai · 1995
This paper proposes an algorithm for speech parameter generation from continuous mixture HMMs which include dynamic features, i.e., delta and delta-delta parameters of speech. We showthatthe parameter generation from HMMs using the dynamic features results in searching for the optimal state sequence and solving a set of linear equations for each possible state sequence. Tosolve the problem, we derivea fast algorithm on the analogy of the RLS algorithm for adaptive #ltering. We show that the generated speech parameter vectors re#ect not only the means of static and dynamic feature vectors but also the covariances of those. An example presenting e#ectiveness of the proposed algorithm in speech synthesis is given. 1. INTRODUCTION The hidden Markov models #HMMs# can model sequences of speech spectra with well-de#ned algorithms, and have successfully been applied to speech recognition systems. From these facts, we surmise that HMMs are also useful for speech synthesis. Actually, some at...