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Ethio-Semitic language identification using convolutional neural networks with data augmentation

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Abstract

In today’s digital world, natural language is used to exchange information among humans, and it has now advanced to the point of being an evolution criteria for technology. The process of determining which language a speaker is speaking is known as spoken language identification, and it is used for front-end processing in human-computer interaction. In this study, we developed a Language Identification model for Ethio-Semitic languages because Language Identification is an intermediate task for other Natural Language Processing tasks such as speech to text translation, speech to speech translation, speech recognition, and speech information retrieval. We used Convolutional Neural Network with respect to different acoustic features such as Mel-frequency Cepstral Coefficients, mel-spectrogram and combined (Mel-frequency Cepstral Coefficients + mel-spectrogram) features to emphasize critical features for uncomplicated output identification. The study’s primary goal was to identify specific languages such as Amharic, Geez, Guragigna, and Tigrigna. Based on this, the results show that Convolutional Neural Network with augmented data and hybrid features performed better than using Mel-frequency Cepstral Coefficients or Mel-spectrogram features. The proposed model achieved an average performance accuracy of 97%, 97.4% and 99.5% for testing, validation, and training respectively. We consequently reached the conclusion that the combined (Mel- Spectrogram + Mel-frequency Cepstral Coefficients) feature was the most crucial feature.

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Data availability

The datasets generated during and/or analysed during the current study are not publicly available but are available from the corresponding author on reasonable request.

Code availability

Not applicable.

Abbreviations

AMC:

Amhara Media Corporation

ANN:

Artificial Neural Network

ASR:

Automatic Speech Recognition

BLSTM:

Bidirectional Long Short-Term Memory

CNN:

Convolutional Neural Network

GFCC:

Gammatone Frequency Cepstral Coefficient

GMM:

Gaussian mixture model

Ethio-Semitic:

Ethiopian Semitic

LFCC:

Linear Frequency Cepstral Coefficients

LID:

Language Identification

MFCC:

Mel-frequency Cepstral Coefficients

NLP:

Natural Language Processing

OBN:

Oromia Broadcast Network

PCA:

Principal Component Analysis

SVM:

Support Vector Machine

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Funding

Authors declare no funding for this research.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, Gafat Institute of Technology, Debre Tabor University, Debre Tabor, Ethiopia

    Amlakie Aschale Alemu

  2. Department of Information Technology, Gafat Institute of Technology, Debre Tabor University, Debre Tabor, Ethiopia

    Malefia Demilie Melese

  3. Department of Electrical/Electronics and Computer Engineering, Afe Babalola University, Ado-Ekiti, Nigeria

    Ayodeji Olalekan Salau

  4. Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai, Tamil Nadu, India

    Ayodeji Olalekan Salau

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  2. Malefia Demilie Melese
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  3. Ayodeji Olalekan Salau
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Contributions

Amlakie Aschale Alemu: Conceptualization, Methodology, Software, Visualization, Writing- Original draft preparation. Malefia Demilie Melese: Conceptualization, Software, Visualization, Investigation. Ayodeji Olalekan Salau: Data curation, Software, Methodology, Writing- Reviewing and Editing, Validation.

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Correspondence to Ayodeji Olalekan Salau.

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Alemu, A.A., Melese, M.D. & Salau, A.O. Ethio-Semitic language identification using convolutional neural networks with data augmentation. Multimed Tools Appl 83, 34499–34514 (2024). https://doi.org/10.1007/s11042-023-17094-y

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