Conference Papers
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Item Identification of Speaker-Specific Features to Minimize the Mismatch Outcomes for Speaker Recognition Using Anger and Happy Emotional Speech(Springer Science and Business Media Deutschland GmbH, 2025) Tomar, S.; Koolagudi, S.G.A vital component of digital speech processing is Speaker Recognition (SR). However, variation in speakers’ emotional states, such as happiness, anger, sadness, or fear, poses a significant challenge that compromises the robustness of speaker recognition systems. It appears to be challenging to distinguish between emotions like “anger†and “happy†, according to research on SR using emotive speech. The study looks at prosody-related speech characteristics to determine how to distinguish between “anger† and “happy†emotional speech for SR tasks. The goal is to explore speaker-specific features. The experiment outcomes demonstrate that, as speaker-specific features for the SR task, Intensity, Pitch, and Brightness (IPB) variables can distinguish between angry and happy emotional speech. Combining IPB and MFCC (IPBCC) feature extraction with the Hybrid CNN-LSTM combined with an attention mechanism approach achieves an SR accuracy of 95.45% for anger and 96.22% for happy emotional speech. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.Item Transformation of Emotional Speech to Anger Speech to Reduce Mismatches in Testing and Enrollment Speech for Speaker Recognition System(Springer Science and Business Media Deutschland GmbH, 2025) Tomar, S.; Koolagudi, S.G.Speaker Recognition (SR) is a critical component of digital speech processing. The robustness of Speaker Recognition systems is compromised by the variance in speakers’ emotional states. According to a study on SR utilizing emotive speech, it seems complicated to distinguish between emotions like “anger,†“sad,†“fear,†and “happy†. Developing a speaker recognition model that works effectively using emotional speech is challenging, specifically in the case of some intense emotions like anger. This work explores emotional speech transformation approaches to reduce the mismatch between training and testing emotional speech for the SR tasks. The recommended effort aims to develop speech transformation techniques to transform different emotional speech into anger. This study modifies the prosodic features “TPIB†(Tempo, Pitch, Intensity, and Brightness) to transform the speech from neutral, happy, fearful, and sad emotions to anger. Performance evaluations of the SR system employing transformed emotional speech are obtained through integrating Mel-Spectrogram feature extraction and deep learning techniques, including the CREMA-D and NITK-KLESC datasets. The experiment results demonstrate that the suggested emotional speech transformation technique increases SR accuracy in transforming neutral by approximately 15%, happy by 11%, sad by 32%, and fear by 30%. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.