This paper presents a cross-language development method for speech recognition and synthesis applications for Macedonian language. Unified system for speech recognition and synthesis trained on German language data was used for acoustic model bootstrapping and adaptation. Both knowledge-based and data-driven approaches for source and target language phoneme mapping were used for initial transcription and labeling of small amount of recorded speech. The recognition experiments on the source language acoustic model with target language dataset showed significant recognition performance degradation. Acceptable performance was achieved after Maximum a posteriori (MAP) model adaptation with limited amount of target language data, allowing suitable use for small to medium vocabulary speech recognition applications. The same unified system was used again to train new separate acoustic model for HMM based synthesis. Qualitative analysis showed, despite the low quality of the available recordings and sub-optimal phoneme mapping, that HMM synthesis produces perceptually good and intelligible synthetic speech.
speech recognition speech synthesis cross-language bootstrapping