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        <title>Explicit Tone Transcription Improves ASR Performance in Extremely Low-Resource Languages: A Case Study in Bribri</title>
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        <namePart type="given">Rolando</namePart>
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            <title>Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas</title>
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    <abstract>Linguistic tone is transcribed for input into ASR systems in numerous ways. This paper shows a systematic test of several transcription styles, using as an example the Chibchan language Bribri, an extremely low-resource language from Costa Rica. The most successful models separate the tone from the vowel, so that the ASR algorithms learn tone patterns independently. These models showed improvements ranging from 4% to 25% in character error rate (CER), and between 3% and 23% in word error rate (WER). This is true for both traditional GMM/HMM and end-to-end CTC algorithms. This paper also presents the first attempt to train ASR models for Bribri. The best performing models had a CER of 33% and a WER of 50%. Despite the disadvantage of using hand-engineered representations, these models were trained on only 68 minutes of data, and therefore show the potential of ASR to generate further training materials and aid in the documentation and revitalization of the language.</abstract>
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    <identifier type="doi">10.18653/v1/2021.americasnlp-1.20</identifier>
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%0 Conference Proceedings
%T Explicit Tone Transcription Improves ASR Performance in Extremely Low-Resource Languages: A Case Study in Bribri
%A Coto-Solano, Rolando
%S Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas
%D 2021
%8 June
%I Association for Computational Linguistics
%C Online
%F coto-solano-2021-explicit
%X Linguistic tone is transcribed for input into ASR systems in numerous ways. This paper shows a systematic test of several transcription styles, using as an example the Chibchan language Bribri, an extremely low-resource language from Costa Rica. The most successful models separate the tone from the vowel, so that the ASR algorithms learn tone patterns independently. These models showed improvements ranging from 4% to 25% in character error rate (CER), and between 3% and 23% in word error rate (WER). This is true for both traditional GMM/HMM and end-to-end CTC algorithms. This paper also presents the first attempt to train ASR models for Bribri. The best performing models had a CER of 33% and a WER of 50%. Despite the disadvantage of using hand-engineered representations, these models were trained on only 68 minutes of data, and therefore show the potential of ASR to generate further training materials and aid in the documentation and revitalization of the language.
%R 10.18653/v1/2021.americasnlp-1.20
%U https://aclanthology.org/2021.americasnlp-1.20
%U https://doi.org/10.18653/v1/2021.americasnlp-1.20
%P 173-184

Markdown (Informal)

[Explicit Tone Transcription Improves ASR Performance in Extremely Low-Resource Languages: A Case Study in Bribri](https://aclanthology.org/2021.americasnlp-1.20) (Coto-Solano, AmericasNLP 2021)

• Explicit Tone Transcription Improves ASR Performance in Extremely Low-Resource Languages: A Case Study in Bribri (Coto-Solano, AmericasNLP 2021)

ACL

• Rolando Coto-Solano. 2021. Explicit Tone Transcription Improves ASR Performance in Extremely Low-Resource Languages: A Case Study in Bribri. In Proceedings of the First Workshop on Natural Language Processing for Indigenous Languages of the Americas, pages 173–184, Online. Association for Computational Linguistics.