Amazigh Speech Recognition via Parallel CNN Transformer-Encoder Model

Mohamed Daouad (), Fadoua Ataa Allah () and El Wardani Dadi ()
Additional contact information
Mohamed Daouad: University of Abdelmalek Essaadi
Fadoua Ataa Allah: CEISIC, The Royal Institute of Amazigh Culture
El Wardani Dadi: University of Abdelmalek Essaadi

A chapter in Information Systems and Technological Advances for Sustainable Development, 2024, pp 255-263 from Springer

Abstract: Abstract Speech recognition technologies serve as a cornerstone in advancing the capabilities of artificial intelligence systems, providing a direct and efficient means of human communication. In the pursuit of fostering a more seamless and intuitive interaction between humans and automated systems, the incorporation of the Amazigh language into Automatic Speech Recognition technology holds profound implications. This integration not only contributes to the preservation of linguistic diversity but also facilitates enhanced accessibility while mitigating the complexities inherent in cross-language ASR transfer. In our research endeavor, we employ a novel approach combining convolutional neural networks with a Transformer encoder network to effectively capture spatial and temporal features for audio classification. Leveraging a dataset comprising 11,644 audio files representing 300 classes, encompassing 200 isolated words and 100 short sentences, recorded from native speakers of the Amazigh Tarifit language, our proposed model demonstrates promising results. Specifically, our model achieves a precision of 84.45% on the test set, underscoring its efficacy in accurately classifying Amazigh speech inputs.

Keywords: Amazigh language; Speech Recognition; parallel deep learning; Transformer; Convolutional Neural Networks (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-031-75329-9_28

Ordering information: This item can be ordered from
http://www.springer.com/9783031753299

DOI: 10.1007/978-3-031-75329-9_28

Access Statistics for this chapter

More chapters in Lecture Notes in Information Systems and Organization from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().