Improved Noise-Resilient Isolated Words Speech Recognition Using Piecewise Differentiation

Speech is the primary method of communication among humans; it is the main form of communication to deliver emotions and thoughts. Intuitively, humans would find it convenient to communicate with machines through speech. The automatic speech recognition model’s primary goal is to transcribe or identify the word sequence represented by the acoustic signals or speech. In this advanced era of digital media and e-learning platforms, the effective use of voice recognition technology to deliver a more individualized and effective educational experience in e-learning initiatives. It helps the students to improve their oral pronunciation skills. Even though Arabic is one of the most spoken languages, the research works related to Arabic speech and text are lacking when compared to other languages. Here, we propose a hybrid model of K-Nearest Neighbor (KNN) classifier and Dynamic Time Warping (DTW) for implementing a noise-resilient speech recognition system for isolated words in the Arabic language. The model is implemented with Mel-Frequency Cepstral Coefficients (MFCC) and its piecewise first and second derivatives as feature representation models. The proposed model is implemented in Python and simulated using Arabic Speech Corpus for Isolated Words [A. Alalshekmubarak and L. Smith, On improving the classification capability of reservoir computing for Arabic speech recognition, in International Conference on Artificial Neural Networks (Springer, Cham, 2014), pp. 225–232]. The proposed model is implemented with (1) MFCC alone for feature representation, (2) combination of MFCC with Delta coefficients, and (3) combination of MFCC with Delta and Delta–Delta coefficients. The implemented model is evaluated using different test sets of varying sizes of 100, 200, 500, 1000, 1500, and 2000. Evaluation is performed for both noised and noiseless speech using these three feature representation models and performed an evaluation of these three models. For evaluating this model’s performance in noised conditions, both white and babble noises of various signal-to-noise ratio values such as 10 dB, 20 dB, and 30 dB are added into the noiseless speech and estimate the classification accuracy in those situations. The proposed KNN–DTW model with MFCC outperformed all the other models in the literature. MFCC with Delta and Delta–Delta coefficients is effective than other two models using MFCC and MFCC with Delta coefficients. The proposed model could be used to recognize the isolated word recognition of grade 1 textbook vocabulary in classroom noisy environment for providing a better interactive classroom environment to Kuwaiti elementary students to enhance their math learning level. In an interactive computer-based education environment for Mathematics learning, the proposed model could be recognized as the spoken utterances of students.