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@article{193729,
        author = {Ms B Keerthana and K Aswini and Syed Ruksar Kausar and Kanna Priyadarshini and Golla Pranay Kumar and Kondapalli Reddy Nikhil},
        title = {SPEECH BASED AGE AND GENDER CLASSIFICATION USING DEEP NEURAL NETWORKS},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {1649-1656},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=193729},
        abstract = {Speech-based age and gender classification represents a rapidly advancing field in human-computer interaction, speaker recognition, and biometric analysis. This research project develops an advanced deep learning framework for automatic age group and gender classification from voice recordings using Long Short-Term Memory (LSTM) networks. The system processes continuous speech through video preprocessing that extracts acoustic features including Mel-Frequency Cepstral Coefficients (MFCCs), pitch contours, formants, and spectral characteristics. The model demonstrates superior performance through comprehensive preprocessing including noise reduction, spectrogram normalization, and data augmentation techniques. Deep neural networks enable automatic extraction of discriminative voice patterns that differentiate age demographics and gender characteristics with high precision. The system supports real-time applications including personalized voice assistants, demographic analytics, content filtering systems, and speaker profiling in security applications. Performance evaluation utilizes accuracy, precision, recall, and F1-score metrics across standardized speech corpora. The proposed framework establishes reliable voice-based demographic classification enabling seamless integration into diverse speech processing ecosystems and assistive technologies.},
        keywords = {speech recognition, age classification, gender classification, deep learning, LSTM, MFCC, speaker demographics, acoustic features, voice biometrics, audio preprocessing, real-time classification, biometric analysis.},
        month = {March},
        }