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Gender recognition in text-independent speaker identification using MFCC, spectrogram, Bi-LSTM, and rat swarm evolutionary algorithm optimization

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Abstract

Text-independent speaker recognition is identifying speakers using their voice characteristics, irrespective of the content spoken. This research paper introduces a new method for this type of recognition by combining Mel-frequency cepstral coefficients (MFCCs), bidirectional long short-term memory (Bi-LSTM) networks, and feature optimization based on the Rat Swarm Optimizer (RSO). MFCCs are first extracted from speech signals as the primary feature set, capturing the vital acoustic features of the speaker's voice. To model temporal dependencies and improve speaker discrimination, a Bi-LSTM network is employed, which captures both forward and backward context in sequential data. The performance of the recognition system is further enhanced by optimizing the extracted features using the Rat Swarm Evolutionary Algorithm, a nature-inspired optimization technique that adapts the feature set to enhance accuracy. The outcomes of the research study on benchmark datasets demonstrate the usefulness of the proposed system, as it produces better results than traditional methods. The overall accuracy of speaker identification is 99.02% and the accuracies for gender recognition i.e, for male (96.72%) and female (96.91%) speakers, confirming the model's robustness across different speaker groups. The integration of Bi-LSTM with RSO feature optimization presents a robust and efficient solution for text-independent speaker recognition in real-world scenarios.

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  1. Chhatrapati Shahu Ji Maharaj University, Kanpur, India

    Manish Tiwari & Deepak Kumar Verma

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  1. Manish Tiwari
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Both authors equally contributed to the manuscript.

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Correspondence to Manish Tiwari.

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Tiwari, M., Verma, D.K. Gender recognition in text-independent speaker identification using MFCC, spectrogram, Bi-LSTM, and rat swarm evolutionary algorithm optimization. Int J Speech Technol 28, 245–260 (2025). https://doi.org/10.1007/s10772-025-10176-2

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