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Deep neural architectures for dialect classification with single frequency filtering and zero-time windowing feature

Rashmi Kethireddy¹, Sudarsana Reddy Kadiri², Suryakanth V Gangashetty³

¹Speech Processing Laboratory, International Institute of Information Technology-Hyderabad (IIIT-H), 500032, India.

The Journal of the Acoustical Society of America

|March 2, 2022

Summary

Advanced signal processing with deep learning models significantly improves English dialect classification. New features like single frequency filtering and zero-time windowing enhance accuracy, outperforming traditional methods.

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Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

Speech Processing
Machine Learning
Linguistics

Background:

Dialect classification is crucial for understanding linguistic variations.
Traditional methods often struggle with subtle spectro-temporal nuances.
Single Frequency Filtering (SFF) and Zero-Time Windowing (ZTW) offer enhanced resolution.

Purpose of the Study:

To investigate advanced signal processing (SFF, ZTW) with Deep Neural Networks (DNNs) for English dialect classification.
To evaluate novel feature representations against established methods.
To assess the performance of various DNN architectures (CNN, TCN, TDNN, ECAPA-TDNN).

Main Methods:

Extracted four feature representations (Spectrogram, Cepstral Coefficients, Mel Filter-Bank Energies, MFCCs) using SFF and ZTW.
Employed CNN, TCN, TDNN, and ECAPA-TDNN classifiers.
Conducted experiments with and without data augmentation on the UT-Podcast database.

Main Results:

Proposed SFF/ZTW features outperformed baseline STFT features by 15-20% without data augmentation.
TCN, TDNN, and ECAPA-TDNN models further improved performance, especially with SFF-derived features.
Best accuracy (85.48%) achieved using Single Frequency Filtered Cepstral Coefficients with ECAPA-TDNN.

Conclusions:

Advanced signal processing techniques (SFF, ZTW) combined with DNNs are highly effective for dialect classification.
Novel feature representations offer significant improvements over traditional methods.
The study provides a robust framework and open-source code for future research in dialect identification.