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ChildAugment: Data augmentation methods for zero-resource children's speaker verification.

Vishwanath Pratap Singh¹, Md Sahidullah^2,3, Tomi Kinnunen¹

¹School of Computing, University of Eastern Finland, Joensuu 80130, Finland.

The Journal of the Acoustical Society of America

|March 26, 2024

Summary

This summary is machine-generated.

ChildAugment improves automatic speaker verification (ASV) for children by augmenting adult speech data to mimic child vocal tracts. This data augmentation technique significantly enhances ASV system accuracy for children's voices.

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Area of Science:

Speech processing
Biometrics
Machine learning

Background:

Automatic speaker verification (ASV) systems trained on adult speech perform poorly on children's speech due to data scarcity.
Effective reuse of adult speech data is crucial for developing accurate ASV for children.
Children-specific data augmentation is a promising approach to bridge the performance gap.

Purpose of the Study:

To introduce and evaluate ChildAugment, a novel data augmentation method for children's speech.
To improve the accuracy of deep learning-based ASV systems for children.
To compare ChildAugment with existing data augmentation techniques and scoring methods.

Main Methods:

ChildAugment modifies adult speech spectra by adjusting formant frequencies and bandwidths to emulate children's vocal tract characteristics.
Augmented data is used to train time-delay neural network (TDNN) recognizers with emphasized channel attention, propagation, and aggregation.
The study compares ChildAugment against state-of-the-art augmentation methods and evaluates various scoring techniques, including cosine scoring, PLDA, and neural PLDA.

Main Results:

ChildAugment demonstrates significant improvements in ASV accuracy for children's speech, achieving up to 12.45% relative improvement for boys and 11.96% for girls.
The proposed low-complexity weighted cosine score shows promise for extremely low-resource children ASV scenarios.
The findings highlight ChildAugment as an effective, acoustics-motivated approach for enhancing children's ASV.

Conclusions:

ChildAugment is a simple yet effective data augmentation strategy for improving deep learning-based ASV systems for children.
The method successfully leverages adult speech data by acoustically adapting it to better represent children's speech characteristics.
The study provides reproducible evaluation protocols and code, facilitating further research in children's speaker verification.