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1. Leveraging End-to-End ASR for Endangered Language Documentation: An Empirical Study on Yoloxochitl Mixtec

   Shi, Jiatong; Amith, Jonathan D.; Castillo Garcia, Rey; Guadalupe Sierra, Esteban; Duh, Kevin; Watanabe, Shinji (January 2020, 2021 Conference of the European Chapter of the Association for Computational Linguistics, 21–23 April. https://arxiv.org/abs/2101.10877)

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   “Transcription bottlenecks”, created by a shortage of effective human transcribers are one of the main challenges to endangered language (EL) documentation. Automatic speech recognition (ASR) has been suggested as a tool to overcome such bottlenecks. Following this suggestion, we investigated the effectiveness for EL documentation of end-to-end ASR, which unlike Hidden Markov Model ASR systems, eschews linguistic resources but is instead more dependent on large-data settings. We open source a Yoloxochitl Mixtec EL corpus. First, we review our method in building an end-to-end ASR system in a way that would be reproducible by the ASR community. We then propose a novice transcription correction task and demonstrate how ASR systems and novice transcribers can work together to improve EL documentation. We believe this combinatory methodology would mitigate the transcription bottleneck and transcriber shortage that hinders EL documentation. 

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2. An Evaluation of Croatian ASR Models for Čakavian Transcription

   Zhang, Shulin; Hale, John; Renwick, Margaret; Vrzić, Zvjezdana; Langston, Keith (May 2024, ACL Anthology)

   To assist in the documentation of Čakavian, an endangered language variety closely related to Croatian, we test four currently available ASR models that are trained with Croatian data and assess their performance in the transcription of Čakavian audio data. We compare the models’ word error rates, analyze the word-level error types, and showcase the most frequent Deletion and Substitution errors. The evaluation results indicate that the best-performing system for transcribing Čakavian was a CTC-based variant of the Conformer model. 

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3. Fully Convolutional ASR for Less-Resourced Endangered Languages

   Thai, Bao; Jimerson, Robert; Ptucha, Raymond; Prud’hommeaux, Emily (May 2020, Proceedings of the 1st Joint Workshop on Spoken Language Technologies for Under-resourced languages (SLTU) and Collaboration and Computing for Under-Resourced Languages (CCURL))

   The application of deep learning to automatic speech recognition (ASR) has yielded dramatic accuracy increases for languages with abundant training data, but languages with limited training resources have yet to see accuracy improvements on this scale. In this paper, we compare a fully convolutional approach for acoustic modelling in ASR with a variety of established acoustic modeling approaches. We evaluate our method on Seneca, a low-resource endangered language spoken in North America. Our method yields word error rates up to 40% lower than those reported using both standard GMM-HMM approaches and established deep neural methods, with a substantial reduction in training time. These results show particular promise for languages like Seneca that are both endangered and lack extensive documentation. 

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4. Developing a spoken dialogue system for the Choctaw language

   Brixey, Jacqueline; Traum, David (February 2023, International Workshop on Spoken Dialogue Systems (IWSDS-2023))

   Abstract We present the first spoken dialogue system for the Choctaw language in this paper. Choctaw is an endangered American indigenous language spoken by the Choctaw tribe. Previous work in this area created a text-based English-Choctaw bilingual chatbot, named Masheli, that primarily shared stories about animals. Ad- ditional work developed an automatic speech recognizer (ASR) to process spoken Choctaw. In this paper, we demo the Choctaw ASR together with the Masheli chat- bot to form a dialogue system that allows the user to speak, rather than type, to the system. As the language is endangered, a spoken dialogue system would assist revitalization efforts by promoting oral fluency in language learners. 

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5. Improving ASR Output for Endangered Language Documentation

   https://doi.org/10.21437/SLTU.2018-39  

   Jimerson, Robert; Simha, Kruthika; Ptucha, Ray; Prud'hommeaux, Emily (August 2018, The 6th Intl. Workshop on Spoken Language Technologies for Under-Resourced Languages)

   Documenting endangered languages supports the historical preservation of diverse cultures. Automatic speech recognition (ASR), while potentially very useful for this task, has been underutilized for language documentation due to the challenges inherent in building robust models from extremely limited audio and text training resources. In this paper, we explore the utility of supplementing existing training resources using synthetic data, with a focus on Seneca, a morphologically complex endangered language of North America. We use transfer learning to train acoustic models using both the small amount of available acoustic training data and artificially distorted copies of that data. We then supplement the language model training data with verb forms generated by rule and sentences produced by an LSTM trained on the available text data. The addition of synthetic data yields reductions in word error rate, demonstrating the promise of data augmentation for this task. 

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