Affiliation:
1. Anna University, Chennai, India
Abstract
The accent spoken by the people is generally influenced by their native mother tongue language. People located at various geographical locations speak by adding flavors to their native language. Various Indian native English accents are classified to bring out a classic difference between these accents. To bring a solution to this problem, a comparative classification model has been built to classify the accents of five distinct native Indian languages such as Tamil, Malayalam, Odia, Telugu, and Bangla from English accents. Firstly, the features of the five-second audio samples each from different accents are obtained and converted to images. The consolidated attributes are gathered. The VGG16 pre-trained model is fused with support vector model to classify accents accurately. Secondly, along with these features, mel frequency cepstral coefficient is added and trained. Then, the features obtained from VGG16 were reduced using principal component analysis. Highest accuracy obtained was 98.46%. Further analysis could be made to produce automated speech recognition for various aspects.