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CNN based keyword spotting: An application for context based voiced Odia words

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Abstract

In recent times, pre-trained convolutional neural networks (CNN) have outdid traditional automatic speech recognition systems based on Gaussian mixture model (GMM) on a variety of large vocabulary benchmarks. The proposed work presents, Odia keyword recognition using CNN approach. Different parts of a human body in spoken Odia language is considered as the keywords for recognition. MFCC feature extraction technique along with spectrogram representation of voiced keywords are considered as the input to the CNN. The projected CNN model is trained and implemented using a python frame work Keras with Tensorflow as backend. Various performance metrics are considered to compare the proposed model with a fully connected deep neural network model (DNN). A number of experiments are conducted with variation of epochs and split ratio (training-validation-testing) and results are obtained to show the accuracy, loss and other performance metrics of both the models. It is observed that the proposed model outperforms the DNN model. Further the average recognition accuracy of proposed CNN model is analyzed with other approaches like (hidden Markov model) HMM and SVM (support vector machine) model and manifests superior average recognition rate as opposed to considered state-of-the-art approaches.

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Authors and Affiliations

  1. Department of CSIT, ITER, S’O’A (Deemed to be) University, Bhubaneswar, Odisha, India

    Prithviraj Mohanty & Ajit Kumar Nayak

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  1. Prithviraj Mohanty
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  2. Ajit Kumar Nayak
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Correspondence to Prithviraj Mohanty.

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Mohanty, P., Nayak, A.K. CNN based keyword spotting: An application for context based voiced Odia words. Int. j. inf. tecnol. 14, 3647–3658 (2022). https://doi.org/10.1007/s41870-022-00992-z

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