Speaker-based language identification for Ethio-Semitic languages using CRNN and hybrid features.

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Publisher: Informa Healthcare Country of Publication: England NLM ID: 9431867 Publication Model: Print-Electronic Cited Medium: Internet ISSN: 1361-6536 (Electronic) Linking ISSN: 0954898X NLM ISO Abbreviation: Network Subsets: MEDLINE

Publication: London : Informa Healthcare
Original Publication: Bristol : IOP Pub., c1990-

Natural language is frequently employed for information exchange between humans and computers in modern digital environments. Natural Language Processing (NLP) is a basic requirement for technological advancement in the field of speech recognition. For additional NLP activities like speech-to-text translation, speech-to-speech translation, speaker recognition, and speech information retrieval, language identification (LID) is a prerequisite. In this paper, we developed a Language Identification (LID) model for Ethio-Semitic languages. We used a hybrid approach (a convolutional recurrent neural network (CRNN)), in addition to a mixed (Mel frequency cepstral coefficient (MFCC) and mel-spectrogram) approach, to build our LID model. The study focused on four Ethio-Semitic languages: Amharic, Ge'ez, Guragigna, and Tigrinya. By using data augmentation for the selected languages, we were able to expand our original dataset of 8 h of audio data to 24 h and 40 min. The proposed selected features, when evaluated, achieved an average performance accuracy of 98.1%, 98.6%, and 99.9% for testing, validation, and training, respectively. The results show that the CRNN model with (Mel-Spectrogram + MFCC) combination feature achieved the best results when compared to other existing models.

Keywords: CRNN; Ethio-Semitic languages; LID; MFCC; Mel-spectrogram

Date Created: 20240604 Latest Revision: 20240604

20240604

10.1080/0954898X.2024.2359610

38832629