Abstract
This paper presents a new method for statistical modelling of prosody control in speech synthesis. The proposed method, which is referred to as Constrained Tree Regression (CTR), can make suitable representation of complex effects of control factors for prosody with a moderate amount of learning data. It is based on recursive splits of predictor variable spaces and partial imposition of constraints of linear independence among predictor variables. It incorporates both linear and tree regressions with categorical predictor variables, which have been conventionally used for prosody control, and extends them to more general models. In addition, a hierarchical error function is presented to consider hierarchical structure in prosody control. This new method is applied to modelling of speech segmental duration. Experimental results show that better duration models are obtained by using the proposed regression method compared with linear and tree regressions using the same number of free parameters. It is also shown that the hierarchical structure of phoneme and syllable durations can be represented efficiently using the hierarchical error function.
| Original language | English |
|---|---|
| Pages (from-to) | 1550-1559 |
| Number of pages | 10 |
| Journal | IEICE Transactions on Information and Systems |
| Volume | E83-D |
| Issue number | 7 |
| Publication status | Published - 2000 Jul |
| Externally published | Yes |
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ASJC Scopus subject areas
- Information Systems
- Computer Graphics and Computer-Aided Design
- Software
Cite this
Statistical modelling of speech segment duration by constrained tree regression. / Iwahashi, Naoto; Sagisaka, Yoshinori.
In: IEICE Transactions on Information and Systems, Vol. E83-D, No. 7, 07.2000, p. 1550-1559.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Statistical modelling of speech segment duration by constrained tree regression
AU - Iwahashi, Naoto
AU - Sagisaka, Yoshinori
PY - 2000/7
Y1 - 2000/7
N2 - This paper presents a new method for statistical modelling of prosody control in speech synthesis. The proposed method, which is referred to as Constrained Tree Regression (CTR), can make suitable representation of complex effects of control factors for prosody with a moderate amount of learning data. It is based on recursive splits of predictor variable spaces and partial imposition of constraints of linear independence among predictor variables. It incorporates both linear and tree regressions with categorical predictor variables, which have been conventionally used for prosody control, and extends them to more general models. In addition, a hierarchical error function is presented to consider hierarchical structure in prosody control. This new method is applied to modelling of speech segmental duration. Experimental results show that better duration models are obtained by using the proposed regression method compared with linear and tree regressions using the same number of free parameters. It is also shown that the hierarchical structure of phoneme and syllable durations can be represented efficiently using the hierarchical error function.
AB - This paper presents a new method for statistical modelling of prosody control in speech synthesis. The proposed method, which is referred to as Constrained Tree Regression (CTR), can make suitable representation of complex effects of control factors for prosody with a moderate amount of learning data. It is based on recursive splits of predictor variable spaces and partial imposition of constraints of linear independence among predictor variables. It incorporates both linear and tree regressions with categorical predictor variables, which have been conventionally used for prosody control, and extends them to more general models. In addition, a hierarchical error function is presented to consider hierarchical structure in prosody control. This new method is applied to modelling of speech segmental duration. Experimental results show that better duration models are obtained by using the proposed regression method compared with linear and tree regressions using the same number of free parameters. It is also shown that the hierarchical structure of phoneme and syllable durations can be represented efficiently using the hierarchical error function.
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UR - http://www.scopus.com/inward/citedby.url?scp=0034226722&partnerID=8YFLogxK
M3 - Article
AN - SCOPUS:0034226722
VL - E83-D
SP - 1550
EP - 1559
JO - IEICE Transactions on Information and Systems
JF - IEICE Transactions on Information and Systems
SN - 0916-8532
IS - 7
ER -