@inproceedings{abulimiti-schultz-2020-automatic,
    title = "Automatic Speech Recognition for {U}yghur through Multilingual Acoustic Modeling",
    author = "Abulimiti, Ayimunishagu  and
      Schultz, Tanja",
    booktitle = "Proceedings of The 12th Language Resources and Evaluation Conference",
    month = may,
    year = "2020",
    address = "Marseille, France",
    publisher = "European Language Resources Association",
    url = "https://www.csl.uni-bremen.de/cms/images/documents/publications/ay_LREC2020.pdf",
    pages = "6444--6449",
    abstract = "Low-resource languages suffer from lower performance of Automatic Speech Recognition (ASR) system due to the lack of data. As a common approach, multilingual training has been applied to achieve more context coverage and has shown better performance over the monolingual training (Heigold et al., 2013). However, the difference between the donor language and the target language may distort the acoustic model trained with multilingual data, especially when much larger amount of data from donor languages is used for training the models of low-resource language. This paper presents our effort towards improving the performance of ASR system for the under-resourced Uyghur language with multilingual acoustic training. For the developing of multilingual speech recognition system for Uyghur, we used Turkish as donor language, which we selected from GlobalPhone corpus as the most similar language to Uyghur. By generating subsets of Uyghur training data, we explored the performance of multilingual speech recognition systems trained with different sizes of Uyghur and Turkish data. The best speech recognition system for Uyghur is achieved by multilingual training using all Uyghur data (10hours) and 17 hours of Turkish data and the WER is 19.17{\%}, which corresponds to 4.95{\%} relative improvement over monolingual training.",
    language = "English",
    ISBN = "979-10-95546-34-4",
}