An algorithm for speech parameter generation from continuous mixture HMMs with dynamic features

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...