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Perception of Sound Waves

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The pitch of a sound depends on the frequency and the pressure amplitude of the source. Two sounds of the same...

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Author Spotlight: Investigating the Impact of Emotional Prosodies on Voice Recognition and Perception

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A mobile Deep Sparse Wavelet autoencoder for Arabic acoustic unit modeling and recognition.

Sarah A Alzakari¹, Salima Hassairi², Amel Ali Alhussan¹

¹Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia.

|March 4, 2024

Summary

This summary is machine-generated.

A new Deep Sparse Wavelet Network (DSWN) efficiently models acoustic units on mobile devices using deep learning and sparse coding. This method shows promise for speech unit classification, reducing computational load.

Keywords:

Acoustic units Deep learning Deep sparse wavelet networks Mel-frequency cepstral coefficients Mobile architecture Perceptual linear predictive Stacked wavelet autoencoders Wavelet networks

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Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Foreign Accent and Forensic Speaker Identification in Voice Lineups: The Influence of Acoustic Features Based on Prosody

Published on: September 27, 2024

Area of Science:

Speech processing
Machine learning
Signal processing

Background:

Acoustic unit modeling is crucial for speech recognition.
Mobile devices require computationally efficient algorithms.
Integrating deep learning, sparse coding, and wavelet networks offers potential for improved acoustic modeling.

Purpose of the Study:

To introduce a novel Deep Sparse Wavelet Network (DSWN) for acoustic unit modeling.
To design a DSWN suitable for mobile architectures with reduced computational overhead.
To classify and differentiate acoustic units using Mel-frequency cepstral coefficients (MFCC) and perceptual linear predictive (PLP) features.

Main Methods:

Developed a Deep Sparse Wavelet Network (DSWN) by integrating stacked wavelet autoencoders.
Utilized Mel-frequency cepstral coefficients (MFCC) and perceptual linear predictive (PLP) features for speech unit encoding.
Designed deep networks with minimal connections to reduce computational complexity for mobile deployment.

Main Results:

Demonstrated the efficacy of the DSWN system on a segmented corpus of Arabic words.
The proposed DSWN achieved effective classification and differentiation of acoustic units.
The methodology successfully reduced computational overhead for mobile applications.

Conclusions:

The Deep Sparse Wavelet Network (DSWN) provides an effective and computationally efficient approach for acoustic unit modeling on mobile devices.
Further research is needed to assess the generalizability of the DSWN to diverse contexts and speech variations.
Future work will investigate the impact of accents and other speech variations on model performance.