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Feature Fusion and Ablation Analysis in Gender Identification of Preschool Children from Spontaneous Speech

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Abstract

The children below 6 years of age are called preliterate who use speech as one of their primary forms of communication. Fundamental frequency or pitch is a characteristic that is used to classify gender, but young children have reasonably similar pitch due to their immature vocal tract which varies from 215 to 390 Hz for both genders. Most studies for gender identification have utilized pitch and mel frequency cepstral coefficients (MFCC), because of their ability to capture the efficacy of signals. However, the performance of pitch and MFCC on noisy speech signals are poor, and as a result, they fail to accurately detect gender characteristics. Considering this limitation, the proposed work investigates the novel fusion and ablation experimentation of mel frequency cepstral coefficients (MFCC) and gamma-tone frequency cepstral coefficients (GFCC). To enhance the accuracy of a robust text-independent children gender identification model, the cepstral features are combined with the tonal descriptors (pitch and harmonic ratio). The most contributing front-end features were selected by fusion and ablation analysis and distributed to a bagged tree classifier ensemble. To manage the memory requirements, redundant features are trimmed using principle component analysis (PCA). The hyper-parameter optimization is accomplished using the grid search technique to further increase frame-level accuracy. This study is likely to be a forerunner in the field of children’s speech recognition, which has been revealed to be a reliable and accurate method of gender identification.

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Data Availability Statement

The open access data that support the findings of this study is available from the ZENODO repository, “https://zenodo.org/record/200495#.Yit0zXpBxPZ”. More details about the data are given in Sect. 3.

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Acknowledgements

The authors would like to extend their gratitude to VIT-AP University for providing the essential resources necessary to conduct this research at the High-Performance Computing Laboratory.

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  1. School of Electronics Engineering, VIT-AP University, Amaravati, Andhra Pradesh, 522237, India

    Kodali Radha & Mohan Bansal

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  1. Kodali Radha
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  2. Mohan Bansal
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Correspondence to Mohan Bansal.

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Radha, K., Bansal, M. Feature Fusion and Ablation Analysis in Gender Identification of Preschool Children from Spontaneous Speech. Circuits Syst Signal Process 42, 6228–6252 (2023). https://doi.org/10.1007/s00034-023-02399-y

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