Statistical methods for varying the degree of articulation in new HMM-based voices

This paper focuses on the automatic modification of the degree of articulation (hypo/hyperarticulation) of an existing standard neutral voice in the framework of HMM-based speech synthesis. Starting from a source speaker for which neutral, hypo and hyperarticulated speech data are available, two sets of transformations are computed during the adaptation of the neutral speech synthesizer. These transformations are then applied to a new target speaker for which no hypo/hyperarticulated recordings are available. Four statistical methods are investigated, differing in the speaking style adaptation technique (MLLR vs. CMLLR) and in the speaking style transposition approach (phonetic vs. acoustic correspondence) they use. This study focuses on the prosody model although such techniques can be applied to any stream of parameters exhibiting suited interpolability properties. Two subjective evaluations are performed in order to determine which statistical transformation method achieves the better segmental quality and reproduction of the articulation degree.