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For further understanding the wide array of emotions embedded in human speech, we are introducing a strictly-guided simulated emotional speech corpus. In contrast to existing speech corpora, this was constructed by maintaining an equal distribution of 4 long vowels in New Zealand English. This balance is to facilitate emotion related formant and glottal source feature comparison studies. Also, the corpus has 5 secondary emotions and 5 primary emotions. Secondary emotions are important in Human-Robot Interaction (HRI) to model natural conversations among humans and robots. But there are few existing speech resources to study these emotions, which has motivated the creation of this corpus. A large scale perception test with 120 participants showed that the corpus has approximately 70% and 40% accuracy in the correct classification of primary and secondary emotions respectively. The reasons behind the differences in perception accuracies of the two emotion types is further investigated. A preliminary prosodic analysis of corpus shows significant differences among the emotions. The corpus is made public at: github.com/tli725/JL-Corpus.

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With the recent developments in speech synthesis via machine learning, this study explores incorporating linguistics knowledge to visualise and evaluate synthetic speech model training. If changes to the first and second formant (in turn, the vowel space) can be seen and heard in synthetic speech, this knowledge can inform speech synthesis technology developers. A speech synthesis model trained on a large General American English database was fine-tuned into a New Zealand English voice to identify if the changes in the vowel space of synthetic speech could be seen and heard. The vowel spaces at different intervals during the fine-tuning were analysed to determine if the model learned the New Zealand English vowel space. Our findings based on vowel space analysis show that we can visualise how a speech synthesis model learns the vowel space of the database it is trained on. Perception tests confirmed that humans could perceive when a speech synthesis model has learned characteristics of the speech database it is training on. Using the vowel space as an intermediary evaluation helps understand what sounds are to be added to the training database and build speech synthesis models based on linguistics knowledge.