Mixing HMM-based spanish speech synthesis with a CBR for prosody estimation

Hidden Markov Models based text-to-speech (HMM-TTS) synthesis is a technique for generating speech from trained statistical models where spectrum, pitch and durations of basic speech units are modelled altogether. The aim of this work is to describe a Spanish HMMTTS system using an external machine learning technique to help improving the expressiveness. System performance is analysed objectively and subjectively. The experiments were conducted on a reliably labelled speech corpus, whose units were clustered using contextual factors based on the Spanish language. The results show that the CBR-based F0 estimation is capable of improving the HMM-based baseline performance when synthesizing non-declarative short sentences while the durations accuracy is similar with the CBR or the HMM system.