Arabic speech recognition model using Baidu's deep and cluster learning

This study involves extracting the spectrum from the Arabic raw, unlabeled audio signal and producing Mel-frequency cepstral coefficients (MFCCs). The clustering algorithm groups the retrieved MFCCs with analogous features. The K-means clustering technique played a crucial role in our research, enabling the unsupervised categorization of unlabeled Arabic audio data. Employing K-means on the extracted MFCC features allowed us to classify acoustically similar segments into distinct groups without prior knowledge of their characteristics. This initial phase was crucial for understanding the inherent diversity in our diverse sampled dataset. Dynamic Time Warping (DTW) and Euclidean Distance are utilized for illustration. Classification algorithms such as Decision Tree, eXtreme Gradient Boosting (XGBoost), K-Nearest Neighbors (KNN), and Random Forest are used to classify the various classes obtained based on clustering. This study also demonstrates the efficacy of Mozilla's Deep Speech framework for Arabic speech recognition. The core component of deep speech is its neural network architecture, which consists of multiple layers of Recurrent Neural Networks (RNNs). It strives to comprehend the intricate patterns and interactions between spoken sounds and their corresponding textual representations. The clustered labeled Arabic audio dataset, along with transcripts and Arabic Alphabets, is used as input to Baidu's Deep Speech model for training and testing purposes. PyCharm, in conjunction with Python 3.6, is used to build a Dockerfile. Creating, editing, and managing Dockerfiles within PyCharm's IDE is simplified by its functionality and integrated environment. Deep speech provides an eminent Arabic speech recognition quality with reduced loss, word error rate (WER), and character error rate (CER). Baidu's Deep Speech intends to achieve high performance in both end-to-end and isolated speech recognition with good precision and a low word rate and character error rate in a reasonable amount of time. The suggested strategy yielded a loss of 276.147, a word error rate of 0.3720, and a character error rate of 0.0568. This technique increases the accuracy of Arabic automatic speech recognition (ASR).