Search Results for author: Esther Klabbers

Found 7 papers, 0 papers with code

Text-to-Speech for Under-Resourced Languages: Phoneme Mapping and Source Language Selection in Transfer Learning

no code implementations SIGUL (LREC) 2022 Phat Do, Matt Coler, Jelske Dijkstra, Esther Klabbers

ASPF was empirically confirmed to be more effective than language family as a criterion for source language selection, and also to affect the phoneme mapping’s effectiveness.

Cross-Lingual Transfer Transfer Learning

The Effects of Input Type and Pronunciation Dictionary Usage in Transfer Learning for Low-Resource Text-to-Speech

no code implementations1 Jun 2023 Phat Do, Matt Coler, Jelske Dijkstra, Esther Klabbers

Results show that the G2P approach performs largely on par with using a ground-truth dictionary and the phone recognition approach, while performing generally worse, remains a viable option for LRLs less suitable for the G2P approach.

Cross-Lingual Transfer Transfer Learning

Data-augmented cross-lingual synthesis in a teacher-student framework

no code implementations31 Mar 2022 Marcel de Korte, Jaebok Kim, Aki Kunikoshi, Adaeze Adigwe, Esther Klabbers

Both sets of data are then used for student model training, which is trained to retain the naturalness and prosodic variation present in the teacher forced data, while learning the speaker identity from the augmented data.

Efficient neural speech synthesis for low-resource languages through multilingual modeling

no code implementations20 Aug 2020 Marcel de Korte, Jaebok Kim, Esther Klabbers

We found that multilingual modeling can increase the naturalness of low-resource language speech, showed that multilingual models can produce speech with a naturalness comparable to monolingual multi-speaker models, and saw that the target language naturalness was affected by the strategy used to add foreign language data.

Speech Synthesis

BLISS: An Agent for Collecting Spoken Dialogue Data about Health and Well-being

no code implementations LREC 2020 Jelte van Waterschoot, Iris Hendrickx, Arif Khan, Esther Klabbers, Marcel de Korte, Helmer Strik, Catia Cucchiarini, Mari{\"e}t Theune

The goal of Behaviour-based Language-Interactive Speaking Systems (BLISS) is to understand the motivations behind people{'}s happiness by conducting a personalized spoken dialogue based on a happiness model.

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