JNACSISSN:2582-3817

Automatic Speaker Diarization using Deep LSTM in Audio Lecturing of e-Khool Platform

Abstract

The speaker diarization is the process of segmentation and the grouping of the input speech signal into a region based on the identity of the speaker. The main challenge in the speaker diarization method is improving the readability of the speech transcription. Hence, in order to overcome the challenge, a speaker diarization method based on deep LSTM is proposed in this research. Initially, the pre-processing is performed for the removal of the noise from the audio lecturing of E-Khoolusers. Then, Linear Predictive Coding (LPC) is used for the extraction of the efficient features from the audio lectures of the E-Khoolusers. After the extraction of the features, the absence or presence of the speaker in the audio lecture is detected using the VAD technique which is followed by the segmentation of the speaker using the extracted features. Finally, the feature vector is determined and the speaker from the audio lecturing of the E-Khoolusers is clustered using the deepLSTM. The proposed speaker diarization method based on deep LSTM is evaluated using the metrics, such as sensitivity, accuracy and specificity. When compared with the existing speaker diarization methods, the proposed speaker diarization method based on deep LSTM obtained a minimum DER of 0.0623, minimum false alarm rate of 0.0369, and minimum distance of 2546 for varying frame length and obtained a minimum DER of 0.0923, minimum false alarm rate of 0.0869, and minimum distance of 1146 for varying Lambda.

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DOI : https://doi.org/10.46253/jnacs.v3i4.a3

Author information

Affiliations

Associate Professor, Deanship of Information Technology and E-Learning, Shaqra University, Shaqra, Saudi Arabia.

Publisher Information

  • Received

    8 July

  • Revised

    19 September

  • Accepted

    12 October

Keywords
Speaker Diarization, Audio Signal, E-Khool Audio Lecturing, Linear Predictive Coding, Deep Learning Classifier

Publisher : Resbee Publisher