Abstract
Transcribing speech is an important part of language documentation, yet speech recognition technology has not been widely harnessed to aid linguists. We explore the use of a neural network architecture with the connectionist temporal classification loss function for phonemic and tonal transcription in a language documentation setting. In this framework, we explore jointly modelling phonemes and tones versus modelling them separately, and assess the importance of pitch information versus phonemic context for tonal prediction. Experiments on two tonal languages, Yongning Na and Eastern Chatino, show the changes in recognition performance as training data is scaled from 10 minutes up to 50 minutes for Chatino, and up to 224 minutes for Na. We discuss the findings from incorporating this technology into the linguistic workflow for documenting Yongning Na, which show the method's promise in improving efficiency, minimizing typographical errors, and maintaining the transcription's faithfulness to the acoustic signal, while highlighting phonetic and phonemic facts for linguistic consideration.
Original language | English |
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Title of host publication | LREC 2018 - 11th International Conference on Language Resources and Evaluation |
Editors | Hitoshi Isahara, Bente Maegaard, Stelios Piperidis, Christopher Cieri, Thierry Declerck, Koiti Hasida, Helene Mazo, Khalid Choukri, Sara Goggi, Joseph Mariani, Asuncion Moreno, Nicoletta Calzolari, Jan Odijk, Takenobu Tokunaga |
Publisher | European Language Resources Association (ELRA) |
Pages | 3356-3365 |
Number of pages | 10 |
Edition | 1 |
ISBN (Electronic) | 9791095546009 |
Publication status | Published - 1 Jan 2019 |
Event | 11th International Conference on Language Resources and Evaluation, LREC 2018 - Miyazaki, Japan Duration: 7 May 2018 → 12 May 2018 |
Conference
Conference | 11th International Conference on Language Resources and Evaluation, LREC 2018 |
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Country/Territory | Japan |
City | Miyazaki |
Period | 7/05/18 → 12/05/18 |