Sınırlı veriyle HMM tabanlı çapraz-dil konuşmacı uyarlamasında özses ve en yakın komşu kullanımı

Thesis abstract: Cross-lingual speaker adaptation for speech synthesis has many applications, such as use in speech-to-speech translation systems. Here, we focus on cross-lingual adaptation for statistical speech synthesis systems using limited adaptation data. We propose new methods on HMM-based and DNN-based speech synthesis. To that end, for HMM-based speech synthesis we propose two eigenvoice adaptation approaches exploiting a bilingual Turkish-English speech database that we collected. In one approach, eigenvoice weights extracted using Turkish adaptation data and Turkish voice models are transformed into the eigenvoice weights for the English voice models using linear regression. Weighting the samples depending on the distance of reference speakers to target speakers during linear regression was found to improve the performance. Moreover, importance weighting the elements of the eigenvectors during regression further improved the performance. The second approach proposed here is speaker-specific state-mapping which performed signicantly better than the baseline state-mapping algorithm both in objective and subjective tests. Performance of the proposed state mapping algorithm was further improved when it was used with the intra-lingual eigenvoice approach instead of the linear-regression based algorithms used in the baseline system. We propose new unsupervised adaptation method for DNN-based speech synthesis. In this method, using sequence of acoustic features from target speaker, we estimate continuous linguistic features for unlabeled data. Based on objective and subjective experiments, adapted model outperformed the gender-dependent average voice models in terms of quality and similarity.