| Paper: | SP-L1.2 | ||
| Session: | Voice Conversion and Morphing Algorithms for TTS Systems | ||
| Time: | Tuesday, May 18, 15:50 - 16:10 | ||
| Presentation: | Lecture | ||
| Topic: | Speech Processing: Speech Synthesis (including TTS) | ||
| Title: | SPEAKING STYLE ADAPTATION USING CONTEXT CLUSTERING DECISION TREE FOR HMM-BASED SPEECH SYNTHESIS | ||
| Authors: | Junichi Yamagishi; Tokyo Institute of Technology | ||
| Makoto Tachibana; Tokyo Institute of Technology | |||
| Takashi Masuko; Tokyo Institute of Technology | |||
| Takao Kobayashi; Tokyo Institute of Technology | |||
| Abstract: | This paper describes an MLLR-based speaking style adaptation technique for HMM-based speech synthesis. Since speaking styles and emotional expressions are characterized by many segment-based features as well as frame-based features, it is necessary to adapt segment-based features for speaking style adaptation. To achieve segment-based feature adaptation, we utilize context clustering decision trees, which are constructed in the training stage, for tying of regression matrices. Using this technique, we adapt an initial ``reading'' style model to ``joyful'' or ``sad'' styles. Experimental results show that, using 50 adaptation sentences, speech samples generated from adapted models were judged to be similar to the target speaking styles at rates of 92% and 70% for joyful and sad styles, respectively. | ||
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