Abstract
In this paper, a novel architecture is proposed using a convolutional neural network (CNN) and mel frequency cepstral coefficient (MFCC) to identify the speaker in a noisy environment. This architecture is used in a text-independent setting. The most important task in any text-independent speaker identification is the capability of the system to learn features that are useful for classification. We are using a hybrid feature extraction technique using CNN as a feature extractor combined with MFCC as a single set. For classification, we used a deep neural network which shows very promising results in classifying speakers. We made our dataset containing 60 speakers, each speaker has 4 voice samples. Our best hybrid model achieved an accuracy of 87.5%. To verify the effectiveness of this hybrid architecture, we use parameters such as accuracy and precision.
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