Phoneme transcription of endangered languages: an evaluation of recent ASR architectures in the single speaker scenario (2022.findings-acl)
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| Challenge: | Recent work on phonetic transcription is reported to be the bottleneck in endangered languages . however, when a single speaker is involved, small amounts of training are needed . |
| Approach: | They compare automatic speech recognition (ASR) approaches to speaker-dependent phonetic transcription using a common dataset of 11 languages. |
| Outcome: | The proposed system handles morphologically complex languages and writing systems for which no pronunciation dictionary exists. |
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| Challenge: | English ASR now has word error rates comparable to that of human transcriptionists, but only for the handful of the world's 7000 languages with abundant training resources. |
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Jiatong Shi, Jonathan D. Amith, Rey Castillo García, Esteban Guadalupe Sierra, Kevin Duh, Shinji Watanabe
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| Challenge: | a phoneme-level analysis of automatic speech recognition (ASR) is performed on two low-resource, typologically complex East Caucasian languages. |
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OCR Post Correction for Endangered Language Texts (2020.emnlp-main)
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| Challenge: | Currently, there is little to no data available to build natural language processing models for endangered languages. |
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A Survey of Multilingual Models for Automatic Speech Recognition (2022.lrec-1)
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| Challenge: | Automatic Speech Recognition (ASR) systems have achieved human-like performance for a few languages, but the majority of the world’s languages do not have usable systems due to the lack of large speech datasets to train these models. |
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