Enhancing State Mapping-Based Cross-Lingual Speaker Adaptation using Phonological Knowledge in a Data-Driven Manner
| Type of publication: | Idiap-RR |
| Citation: | Liang_Idiap-RR-08-2013 |
| Number: | Idiap-RR-08-2013 |
| Year: | 2013 |
| Month: | 3 |
| Institution: | Idiap |
| Abstract: | HMM state mapping with the Kullback-Leibler divergence as a distribution similarity measure is a simple and effective technique that enables cross-lingual speaker adaptation for speech synthesis. However, since this technique does not take any other potentially useful information into account for mapping construction, an approach involving phonological knowledge in a data-driven manner is proposed in order to produce better state mapping rules – state distributions from the input and output languages are clustered according to broad phonetic categories using a decision tree, and mapping rules are constructed only within each resultant leaf node. Apart from this, previous research shows that a regression class tree that follows the decision tree structure for state tying is detrimental to cross-lingual speaker adaptation. Thus it is also proposed to apply this new approach to regression class tree growth – state distributions from the output language are clustered according to broad phonetic categories using a decision tree, which is then directly used as a regression class tree for transform estimation. Experimental results show that the proposed approach can reduce mel-cepstral distortion consistently and produce state mapping rules and regression class trees that generalize to unseen test speakers. The impacts of the phonological/acoustic similarity between input and output languages upon the reliability of state mapping rules and upon the structure of regression class trees are also demonstrated and analyzed. |
| Keywords: | cross-lingual speaker adaptation, data-driven enhancement, HMM state mapping, minimum generation error, phonological constraints, regression class tree |
| Projects |
Idiap |
| Authors | |
| Added by: | [ADM] |
| Total mark: | 0 |
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