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Abstract
AbstractUntil now, the successive state‐splitting algorithm (SSS) has been proposed as an automatic generation algorithm for the hidden Markov network (HMnet), which can be used as a high‐performance efficient context‐dependent model. SSS is a splitting‐type algorithm in which the HMnet is detailed only by splitting states.On the other hand, the “merge‐type” algorithm also is proposed, where the model is constructed while merging the parameters based on the partial similarity of the model parameters. The two methods have the common goal, but take completely different approaches. Consequently, they should complement each other.From such a viewpoint, this paper introduces the mechanism of the “merge‐type” technique into SSS as the “splitting‐type” algorithm. In other words, the “state‐splitting and merging” algorithm is proposed, where a higher‐performance automatic generation of HMnet is realized by complementing the defects of the two methods. An HMnet actually is constructed using 25 Japanese phoneme samples. By various evaluation experiments, the performance of the proposed algorithm is demonstrated.
Published Version
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