Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Koolagudi, S.G.Subject: search.filters.subject.Convolutional Neural Network

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    CNN-MFCC Model for Speaker Recognition using Emotive Speech
    (Institute of Electrical and Electronics Engineers Inc., 2023) Tomar, S.; Koolagudi, S.G.
    Finding the appropriate speaker using voice recognition is called "speaker recognition."Emotive Environment Speaker Recognition (EESR) identifies speakers using distinct emotional speech. A real-life situation that becomes a requirement for many applications is speaker recognition, which utilizes various moods. If there is no emotion in the conversation, speaker recognition algorithms work almost flawlessly. This work aims to improve the accuracy of text-dependent and emotional speaker recognition system in emotional speech contexts. The proposed method is developed using Mel-Frequency Cepstral Coefficient (MFCC) feature and the classifier considered is Convolutional Neural Networks (CNN) for various emotions. The suggested system's performance is assessed based on emotional datasets from the Kannada Language and Emotional Database (EmoDB). These emotions are present in both datasets: happy, sad, angry, fear, and neutral. Due to the complexity of emotions, speaker recognition in various emotional states is challenging. The proposed system offers an accuracy of 96.2% in the EmoDB and 97.8% in the Kannada dataset. The proposed method provides a high recognition rate for different emotions. © 2023 IEEE.
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    Analysis of Speaker Recognition in Blended Emotional Environment Using Deep Learning Approaches
    (Springer Science and Business Media Deutschland GmbH, 2023) Tomar, S.; Koolagudi, S.G.
    Generally, human conversation has some emotion, and natural emotions are often blended. Today’s Speaker Recognition systems lack the component of emotion. This work proposes a Speaker Recognition approaches in Blended Emotion Environment (SRBEE) system to enhance Speaker Recognition (SR) in an emotional context. Speaker Recognition algorithms nearly always achieve perfect performance in the case of neutral speech, but it is not true from an emotional perspective. This work attempts the recognition of speakers in blended emotion with the Mel-Frequency Cepstral Coefficients (MFCC) feature extraction using the Conv2D classifier. In the blended emotional environment, calculating the accuracy of the Speaker Recognition task is complex. The blend of four basic natural emotions (happy, sad, angry, and fearful) utterances tested in the proposed system to reduce SR’s complexity in a blended emotional environment. The proposed system achieves an average accuracy of 99.3% for blended emotion with neutral speech and 92.8% for four basic blended natural emotions (happy, sad, angry, and fearful). The dataset was prepared by blending two emotions in one utterance. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.
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    Non-Invasive Detection of Anemia Using Deep Learning on Conjunctival Images
    (Institute of Electrical and Electronics Engineers Inc., 2025) Kedar, D.S.; Pandey, G.; Koolagudi, S.G.
    Anemia, characterized by low levels of red blood cells or hemoglobin, affects millions worldwide, significantly affecting public health. Traditional diagnostic methods, while effective, are invasive, costly, and inaccessible in resource-constrained settings. This paper proposes a non-invasive approach for anemia detection using conjunctival images analyzed through deep learning techniques. The proposed methodology involves capturing high-resolution conjunctival images, pre-processing them, and using a customized Convolutional Neural Network (CNN) for feature extraction and classification. The results achieved by the customized CNN fine-tuned with a batch size of 16 give an Accuracy of 96%, Precision of 95%, Recall of 96%, and ROC-AUC score of 0.99. The customized CNN outperformed the other models for this work, such as Random Forest, XGBoost, SVM, ResNet50, and MobileNetV2. This work highlights the potential for non-invasive diagnostic tools to improve accessibility and efficiency in healthcare, particularly for underserved populations. The findings endorse integrating deep learning in healthcare as a transformative approach to address global challenges such as anemia. © 2025 IEEE.