M an W av: The First M anchu ASR Model (2024)

Abstract: This study addresses the widening gap in Automatic Speech Recognition (ASR) research between high resource and extremely low resource languages, with a particular focus on Manchu, a severely endangered language. Manchu exemplifies the challenges faced by marginalized linguistic communities in accessing state-of-the-art technologies. In a pioneering effort, we introduce the first-ever Manchu ASR model ManWav, leveraging Wav2Vec2-XLSR-53. The results of the first Manchu ASR is promising, especially when trained with our augmented data. Wav2Vec2-XLSR-53 fine-tuned with augmented data demonstrates a 0.02 drop in CER and 0.13 drop in WER compared to the same base model fine-tuned with original data.

## introduction the landscape of automatic speech recognition (asr) research has centered around high resource languages such as english. this concentrated attention on high resource languages has deepened the divide between research advancements. while research on english asr encompasses diverse linguistic variations, including accented and noised speech, the same cannot be said for many low resource languages, though a few basic research including @xcite and @xcite exist. astonishingly, not a single basic asr model has been developed for manchu to date, highlighting a critical void in linguistic inclusivity within the realm of asr technology. the development of a manchu asr model holds particular importance in the field of linguistics, as there are no more native speakers of manchu. consequently, the available data, whether text or audio, for linguistic study is limited and cannot be replenished. therefore, it is crucial to maximize the utilization of existing data. however, due to the scarcity of individuals capable of transcribing manchu audio data, unlabeled data remain unused. if transcribed, this data could prove to be invaluable resource for manchu research and preservation. even though the performance of the manchu asr system may not be perfect, it would be immensely helpful if it could provide draft transcriptions. this would enable researchers to revise and incorporate them into their studies. this paper sets out to address the significant gap between high and low resource languages by developing the inaugural manchu asr model. this endeavor is underscored by the scarcity of linguistic resources, prompting us to collect all existing manchu audio data from @xcite in one channel. we try to maximize the cross-lingual capabilities of wav2vec2-xlsr-53 @xcite by fine-tuning the model with manchu audio data. the performance of the manchu asr model is further enhanced through data augmentation. the contributions of this study are as follows: ## manchu language the manchu language, a member of the tungusic linguistic family, has its roots among the manchu people of northeast china and boasts a significant historical role as the official language of the qing dynasty . presently, the language confronts a dire state of endangerment, officially denoted a dead language with no more native speakers left. there have been some efforts to employ technological solutions in the preservation and revitalization of manchu. these endeavors include the manchu spell checker @xcite , manchu-korean machine translation @xcite , and manchu ner/pos tagging models @xcite . however, due to the paucity of data, the studies above face challenges and no asr model has been yet developed. ## conclusion and future work as an extremely low resource language, manchu has often been overlooked in linguistic technology. in an effort to maximize the utilization of available manchu data, the development of an asr system is essential. we introduce manwav, which involves fine-tuning wav2vec2-xlsr-53 on augmented manchu audio data, with the aim of providing a valuable tool for the study and preservation of manchu. as the addition of a decoder to an asr model is known to boost the inference performance @xcite , enhancing the inference quality with the help of a language model should be studied in the future.