Enhanced Speech Recognition for Chhattisgarhi Dialect Using Hybrid CNN-MFCC Models

The field of speech recognition has advanced significantly, particularly in addressing dialectal diversity and lowresource languages such as Chhattisgarhi.This dialect presents unique challenges due to its linguistic characteristics and limited digital resources.Recent research has focused on leveraging deep learning techniques, including Convolutional Neural Networks (CNNs) and Mel-frequency cepstral coefficients (MFCC), to enhance the accuracy of speech recognition systems tailored for Chhattisgarhi.These efforts are crucial for developing applications that can cater to the linguistic diversity of the region and improve automated transcription accuracy.The development of specialized speech corpora has played a pivotal role, providing essential data to train and validate models specific to Chhattisgarhi.Hybrid approaches combining deep learning with traditional techniques show promise in capturing nuanced features of Chhattisgarhi speech and addressing the complexities of dialectal variations.As research continues, these advancements not only contribute to linguistic research but also hold significant implications for developing inclusive technologies that serve diverse linguistic communities.