Noise effect on Amazigh digits in speech recognition system

Automatic Speech Recognition (ASR) for Amazigh speech, particularly Moroccan Tarifit accented speech, is a less researched area. This paper focuses on the analysis and evaluation of the first ten Amazigh digits in the noisy conditions from an ASR perspective based on Signal to Noise Ratio (SNR). Our testing experiments were performed under two types of noise and repeated with added environmental noise with various SNR ratios for each kind ranging from 5 to 45 dB. Different formalisms are used to develop a speaker independent Amazigh speech recognition, like Hidden Markov Model (HMMs), Gaussian Mixture Models (GMMs). The experimental results under noisy conditions show that degradation of performance was observed for all digits with different degrees and the rates under car noisy environment are decreased less than grinder conditions with the difference of 2.84% and 8.42% at SNR 5 dB and 25 dB, respectively. Also, we observed that the most affected digits are those which contain the "S" alphabet.