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@TECHREPORT{Liang_Idiap-RR-08-2013,
         author = {Liang, Hui and Dines, John},
       keywords = {cross-lingual speaker adaptation, data-driven enhancement, HMM state mapping, minimum generation error, phonological constraints, regression class tree},
       projects = {Idiap},
          month = {3},
          title = {Enhancing State Mapping-Based Cross-Lingual Speaker Adaptation using Phonological Knowledge in a Data-Driven Manner},
           type = {Idiap-RR},
         number = {Idiap-RR-08-2013},
           year = {2013},
    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.},
            pdf = {https://publications.idiap.ch/attachments/reports/2013/Liang_Idiap-RR-08-2013.pdf}
}