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A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

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Formant estimation and tracking: A deep learning approach.

Yehoshua Dissen¹, Jacob Goldberger², Joseph Keshet¹

¹Department of Computer Science, Bar-Ilan University, Ramat Gan, 52900, Israel.

The Journal of the Acoustical Society of America

|March 3, 2019

Summary

This summary is machine-generated.

Supervised machine learning effectively estimates and tracks formant frequencies in speech. New network architectures adapt to unseen frequency ranges, improving performance on diverse datasets.

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Area of Science:

Speech processing
Acoustic phonetics
Machine learning

Background:

Formant frequency estimation and tracking are crucial for analyzing speech signals.
Traditional methods face challenges in accuracy and adaptability.

Purpose of the Study:

To propose and evaluate supervised machine learning techniques for formant frequency estimation and tracking.
To develop adaptable network architectures for handling diverse formant frequency ranges.

Main Methods:

Evaluated feed-forward multilayer-perceptrons and convolutional neural networks for estimation.
Utilized recurrent and convolutional recurrent networks for tracking.
Employed linear predictive coding-based cepstral coefficients and raw spectrograms as inputs.
Developed an adaptive network architecture for unseen formant frequency ranges.

Main Results:

Proposed machine learning methods demonstrate competitive performance compared to alternatives.
The adaptive network architecture significantly improved performance on unseen formant frequency ranges.
Evaluations across three datasets confirmed enhanced accuracy and robustness.

Conclusions:

Supervised deep learning offers a powerful approach for formant frequency estimation and tracking.
The proposed adaptive network architecture enhances generalization capabilities for speech analysis.
These advancements hold promise for improved speech processing applications.