Enhancement of spoken digits recognition for under-resourced languages: case of Algerian and Moroccan dialects

Lounnas, Khaled; Abbas, Mourad; Lichouri, Mohamed; Hamidi, Mohamed; Satori, Hassan; Teffahi, Hocine

doi:10.1007/s10772-022-09971-y

Cited by 9 publications

(1 citation statement)

References 27 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Lounnas K. et al [14] reported a series of research using a hybrid methodology to improve spoken digit recognition for under-resourced languages of the Maghreb region. The value of including a dialect identification module in an Automatic Speech Recognition (ASR) system has been shown in earlier research.…”

Section: Ghazi and Dawimentioning

confidence: 99%

Amazigh Spoken Digit Recognition using a Deep Learning Approach based on MFCC

Boulal,

Hamidi,

Abarkan

et al. 2023

Int. j. electr. comput. eng. syst. (Online)

View full text Add to dashboard Cite

The field of speech recognition has made human-machine voice interaction more convenient. Recognizing spoken digits is particularly useful for communication that involves numbers, such as providing a registration code, cellphone number, score, or account number. This article discusses our experience with Amazigh's Automatic Speech Recognition (ASR) using a deep learning- based approach. Our method involves using a convolutional neural network (CNN) with Mel-Frequency Cepstral Coefficients (MFCC) to analyze audio samples and generate spectrograms. We gathered a database of numerals from zero to nine spoken by 42 native Amazigh speakers, consisting of men and women between the ages of 20 and 40, to recognize Amazigh numerals. Our experimental results demonstrate that spoken digits in Amazigh can be recognized with an accuracy of 91.75%, 93% precision, and 92% recall. The preliminary outcomes we have achieved show great satisfaction when compared to the size of the training database. This motivates us to further enhance the system's performance in order to attain a higher rate of recognition. Our findings align with those reported in the existing literature.

show abstract

Section: Ghazi and Dawimentioning

confidence: 99%