Audiobook Dialogues as Training Data for Conversational Style Synthetic Voices
Liisi Piits, Hille Pajupuu, Heete Sahkai, Rene Altrov, Liis Ermus, Kairi Tamuri, Indrek Hein, Meelis Mihkla, Indrek Kiissel, Egert Männisalu, Kristjan Suluste, Jaan Pajupuu
Abstract
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.- Anthology ID:
- 2022.lrec-1.112
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Editors:
- Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 1047–1053
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.112
- DOI:
- Cite (ACL):
- Liisi Piits, Hille Pajupuu, Heete Sahkai, Rene Altrov, Liis Ermus, Kairi Tamuri, Indrek Hein, Meelis Mihkla, Indrek Kiissel, Egert Männisalu, Kristjan Suluste, and Jaan Pajupuu. 2022. Audiobook Dialogues as Training Data for Conversational Style Synthetic Voices. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1047–1053, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Audiobook Dialogues as Training Data for Conversational Style Synthetic Voices (Piits et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-1/2022.lrec-1.112.pdf