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Closed-set automatic speaker identification using multi-scale recurrent networks in non-native children.

Kodali Radha¹, Mohan Bansal¹

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

International Journal of Information Technology : an Official Journal of Bharati Vidyapeeth'S Institute of Computer Applications and Management

|April 14, 2023

Summary

This summary is machine-generated.

This study developed a child speaker identification system for non-native English speakers, improving accuracy in security and education applications. The system effectively tracks fluency

Keywords:

Automatic speaker identification Bi-LSTM Non-native children speech RNN Text-dependent speech Text-independent speech Wavelet scattering transform

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Area of Science:

Speech processing
Biometrics
Machine learning

Background:

Automatic speaker identification (ASI) has applications in child security, safety, and education.
Existing ASI systems often struggle with non-native speakers and children due to fluency variations.
Mel frequency cepstral coefficients (MFCCs) are common but can lose high-frequency information.

Purpose of the Study:

To develop a closed-set child speaker identification system for non-native English speakers.
To evaluate the system's performance in both text-dependent and text-independent speech tasks.
To analyze the impact of speaker fluency on identification accuracy.

Main Methods:

Utilized multi-scale wavelet scattering transform to preserve high-frequency information, overcoming MFCC limitations.
Employed a wavelet scattered Bidirectional Long Short-Term Memory (Bi-LSTM) network for speaker identification.
Assessed system performance using accuracy, precision, recall, and F-measure metrics.

Main Results:

The proposed wavelet scattered Bi-LSTM system achieved high performance in identifying non-native child speakers.
The system demonstrated effectiveness in both text-dependent and text-independent speech tasks.
Performance metrics indicated superiority over existing child speaker identification models.

Conclusions:

The developed ASI system is effective for identifying non-native child speakers across different speech tasks.
The multi-scale wavelet scattering transform and Bi-LSTM architecture enhance robustness to fluency variations.
This technology holds promise for improving child-focused security, safety, and educational applications.