Marathi and Konkani Speech Recognition using Cross-Correlation Analysis
Alvan D'mello, Aishwary Jadhav, Jui Kale, Reena Sonkusare · 2021
Systems that extract information from speech are common in communications and automation, where an individual's voice is analysed and recognised by the system to follow the intended course of action. Extant research on speech recognition deals with the application of complex performance techniques of Artificial Intelligence (AI) and Machine Learning (ML) to study large non-isolated voice patterns. This paper uses simplistic performance parameters, namely, cross correlation for the identification of individual voice samples consisting of isolated Marathi and Konkani words in MATLAB. The study examines a dataset of 90 samples comprising 10 words from both languages for 10 individuals. The study presents a comparative analysis of. wav and. ogg formats of the audio samples with efficiencies of 93.5% and 88.5% for Marathi and Konkani respectively.