| Summary: | Automatic speech recognition (ASR) for low-resource languages is an active field of research. Over the past years with the advent of deep learning, impressive achievements have been reported using minimal resources. As many of the world’s languages are getting extinct every year, with every dying language we lose intellect, culture, values, and tradition which generally pass down for long generations. Linguists throughout the world have already initiated many projects on language documentation to preserve such endangered languages. Automatic speech recognition is a solution to accelerate the documentation process reducing the annotation time for field linguists as well as the overall cost of the project. A traditional speech recognizer is trained on thousands of hours of acoustic data and a phonetic dictionary that includes all words from the language. End-to-End ASR systems have shown dramatic improvement for major languages. Especially, recent advancement in self-supervised representation learning which takes advantage of large corpora of untranscribed speech data has become the state-of-the-art for speech recognition technology. However, for resource-constrained languages, the technology is not tested in depth. In this thesis, we explore both traditional methods of ASR and state-of-the-art end-to-end systems for modeling a critically endangered Athabascan language known as Upper Tanana. In our first approach, we investigate traditional models with a comparative study on feature selection and a performance comparison with deep hybrid models. With limited resources at our disposal, we build a working ASR system based on a grapheme-to-phoneme (G2P) phonetic dictionary. The acoustic model can also be used as a separate forced alignment tool for the automatic alignment of training data. The results show that the GMM-HMM methods outperform deep hybrid models in low-resource acoustic modeling. In our second approach, we propose using Domain-adapted Cross-lingual Speech Recognition (DA-XLSR) for an ASR system, ...
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