@inproceedings{james-etal-2024-development,
    title = "Development of Community-Oriented Text-to-Speech Models for {M}{\=a}ori {`}Avaiki {N}ui ({C}ook {I}slands {M}{\=a}ori)",
    author = "James, Jesin  and
      Coto-Solano, Rolando  and
      Nicholas, Sally Akevai  and
      Zhu, Joshua  and
      Yu, Bovey  and
      Babasaki, Fuki  and
      Wang, Jenny Tyler  and
      Derby, Nicholas",
    editor = "Calzolari, Nicoletta  and
      Kan, Min-Yen  and
      Hoste, Veronique  and
      Lenci, Alessandro  and
      Sakti, Sakriani  and
      Xue, Nianwen",
    booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
    month = may,
    year = "2024",
    address = "Torino, Italia",
    publisher = "ELRA and ICCL",
    url = "https://aclanthology.org/2024.lrec-main.432",
    pages = "4820--4831",
    abstract = {In this paper we describe the development of a text-to-speech system for M{\=a}ori {`}Avaiki Nui (Cook Islands M{\=a}ori). We provide details about the process of community-collaboration that was followed throughout the project, a continued engagement where we are trying to develop speech and language technology for the benefit of the community. During this process we gathered a group of recordings that we used to train a TTS system. When training we used two approaches, the HMM-system MaryTTS (Schr{\"o}der et al., 2011) and the deep learning system FastSpeech2 (Ren et al., 2020). We performed two evaluation tasks on the models: First, we measured their quality by having the synthesized speech transcribed by ASR. The human produced ground truth had lower error rates (CER=4.3, WER=18), but the FastSpeech2 audio has lower error rates (CER=11.8 and WER=42.7) than the MaryTTS voice (CER=17.9 and WER=48.1). The second evaluation was a survey amongst speakers of the language so they could judge the voice{'}s quality. The ground truth was rated with the highest quality (MOS=4.6), but the FastSpeech2 voice had an overall quality of MOS=3.2, which was significantly higher than that of the MaryTTS synthesized recordings (MOS=2.0). We intend to use the FastSpeech2 model to create language learning tools for community members both on the Cook Islands and in the diaspora.},
}

In this paper we describe the development of a text-to-speech system for M{\=a}ori {`}Avaiki Nui (Cook Islands M{\=a}ori). We provide details about the process of community-collaboration that was followed throughout the project, a continued engagement where we are trying to develop speech and language technology for the benefit of the community. During this process we gathered a group of recordings that we used to train a TTS system. When training we used two approaches, the HMM-system MaryTTS (Schr{\"o}der et al., 2011) and the deep learning system FastSpeech2 (Ren et al., 2020). We performed two evaluation tasks on the models: First, we measured their quality by having the synthesized speech transcribed by ASR. The human produced ground truth had lower error rates (CER=4.3, WER=18), but the FastSpeech2 audio has lower error rates (CER=11.8 and WER=42.7) than the MaryTTS voice (CER=17.9 and WER=48.1). The second evaluation was a survey amongst speakers of the language so they could judge the voice{'}s quality. The ground truth was rated with the highest quality (MOS=4.6), but the FastSpeech2 voice had an overall quality of MOS=3.2, which was significantly higher than that of the MaryTTS synthesized recordings (MOS=2.0). We intend to use the FastSpeech2 model to create language learning tools for community members both on the Cook Islands and in the diaspora.
James, Jesin Coto-Solano, Rol o Nicholas, Sally Akevai Zhu, Joshua Yu, Bovey Babasaki, Fuki Wang, Jenny Tyler Derby, Nicholas
Development of Community-Oriented Text-to-Speech Models for Māori `Avaiki Nui (Cook Islands Māori)