Deep Models for Low-Resourced Speech Recognition: Livvi-Karelian Case

• Published in: Mathematics
• Main Authors: Irina Kipyatkova, Ildar Kagirov
• Format: Article in Journal/Newspaper
• Language: English
• Published: MDPI AG 2023
• Subjects: low-resource languages; automatic speech recognition; audio data augmentation; time delay neural network; hidden Markov models; long short-term memory; Mathematics; QA1-939; karelian
• Online Access: https://doi.org/10.3390/math11183814; https://doaj.org/article/d9e0c8bc649d4be88c9f93a1bca08ef9

Recently, there has been a growth in the number of studies addressing the automatic processing of low-resource languages. The lack of speech and text data significantly hinders the development of speech technologies for such languages. This paper introduces an automatic speech recognition system for Livvi-Karelian. Acoustic models based on artificial neural networks with time delays and hidden Markov models were trained using a limited speech dataset of 3.5 h. To augment the data, pitch and speech rate perturbation, SpecAugment, and their combinations were employed. Language models based on 3-grams and neural networks were trained using written texts and transcripts. The achieved word error rate metric of 22.80% is comparable to other low-resource languages. To the best of our knowledge, this is the first speech recognition system for Livvi-Karelian. The results obtained can be of a certain significance for development of automatic speech recognition systems not only for Livvi-Karelian, but also for other low-resource languages, including the fields of speech recognition and machine translation systems. Future work includes experiments with Karelian data using techniques such as transfer learning and DNN language models.