This can be achieved by developing an Automatic Speech Recognition (ASR) system. The ambitious speech research for more than 50 years is having a machine to understand fluently spoken speech. The overall efficiency of the proposed system to recognize the speaker, about the region he belongs, based on accent is 91 %. In the next step Gaussian mixture model (GMM) is used for classification of the speech based on accent. In this work, Mel frequency cepstral coefficients (MFCC) features are extracted for each speech of both training and test samples. In this present work the samples of speeches are collected from the native speakers of different accents of Telugu language for both training and testing. The main accents are coastal Andhra, Telangana, and Rayalaseema. Telugu is an Indian language which is widely spoken in Southern part of India. If the number of accents is more in a language, the accent recognition becomes crucial. Identification of the accent before the speech recognition can improve performance of the speech recognition systems. Many of the languages have different speaking styles called accents or dialects. Speech processing is very important research area where speaker recognition, speech synthesis, speech codec, speech noise reduction are some of the research areas.
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March 2023
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