This paper presents the development and evaluation of an Estonian isolated-word text-to-speech (TTS) synthesiser. Unlike conventional TTS systems that convert continuous text into speech, this system focuses on the synthesis of isolated words, which is crucial for applications such as pronunciation training, speech therapy, and (learners’) dictionaries. The system addresses two key challenges: generating natural prosody for isolated words and context-free disambiguation of homographs. We conducted a perception test to evaluate the performance of the TTS system in terms of pronunciation accuracy. We used 16 pairs of homographs that differ in palatalisation and 16 pairs of homographs that differ in quantity. Given that all the test items were correctly recognised by a majority of the evaluators, the performance of the synthesiser can be considered very good.

Synthetic voices are increasingly used in applications that require a conversational speaking style, raising the question as to which type of training data yields the most suitable speaking style for such applications. This study compares voices trained on three corpora of equal size recorded by the same speaker: an audiobook character speech (dialogue) corpus, an audiobook narrator speech corpus, and a neutral-style sentence-based corpus. The voices were trained with three text-to-speech synthesisers: two hidden Markov model-based synthesisers and a neural synthesiser. An evaluation study tested the suitability of their speaking style for use in customer service voice chatbots. Independently of the synthesiser used, the voices trained on the character speech corpus received the lowest, and those trained on the neutral-style corpus the highest scores. However, the evaluation results may have been confounded by the greater acoustic variability, less balanced sentence length distribution, and poorer phonemic coverage of the character speech corpus, especially compared to the neutral-style corpus. Therefore, the next step will be the creation of a more uniform, balanced, and representative audiobook dialogue corpus, and the evaluation of its suitability for further conversational-style applications besides customer service chatbots.