Abstract
Introducing neural networks into the field has improved the performance of speech synthesis systems significantly. Most research was done on the English language which has substantial speech data resources, however, a low degree of grapheme-phoneme correspondence. Other, low resource languages pose different challenges that may be overcome using different approaches to text embeddings. In the present paper we present the results of using stressed text labels in speech datasets to train a speech synthesis model for a low speech data resource language - Lithuanian. Nvidia’s implementation of the Tacotron 2 system and the Lithuanian language speech dataset (corpus) with stressed text labels were used to train speech synthesis models. By introducing accentuation into sample labels, we show a significant improvement in speech naturalness as measured by MOS.
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Radzevičius, A., Raudys, A., Kasparaitis, P. (2021). Speech Synthesis Using Stressed Sample Labels for Languages with Higher Degree of Phonemic Orthography. In: Lopata, A., Gudonienė, D., Butkienė, R. (eds) Information and Software Technologies. ICIST 2021. Communications in Computer and Information Science, vol 1486. Springer, Cham. https://doi.org/10.1007/978-3-030-88304-1_30
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