A Novel Approach for Speech Recognition of Malayalam Using Enhanced MFCC Features

Abstract

The study Investigates into the realm of speech recognition, particularly focusing on the evaluation of Hidden Markov Models (HMM), leveraging the MalayalamVoice dataset. The research scrutinizes the performance of HMM concerning word error rate (WER) and accuracy across varied word lengths, revealing a consistent trend of increased WER and decreased accuracy with longer utterances. This underscores the challenges inherent in accurately transcribing extended speech segments, accentuating the necessity for algorithmic enhancements. Moreover, analyses across diverse datasets and noisy environments underscore the criticality of comprehending dataset characteristics for refining recognition algorithms. Additionally, comparisons of different feature extraction methods elucidate the efficacy of Enhanced MFCC, particularly for shorter word lengths. However, as the word length extends, the distinctions between extraction methods diminish, highlighting the multifaceted nature of speech recognition. Overall, this study underscores the intricacies involved in speech recognition and the imperative of algorithmic refinements for augmenting accuracy, especially in practical scenarios.