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Identification of speech transients using variable frame rate analysis and wavelet packets.

Daniel M Rasetshwane¹, J Robert Boston, Ching-Chung Li

¹Dept. of Electr. & Comput. Eng., Pittsburgh Univ., PA 15261, USA.

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference

|October 20, 2007

Summary

This summary is machine-generated.

This study introduces an efficient method for identifying and emphasizing speech transients, crucial for speech sound discrimination. The new approach improves speech intelligibility in noise without high computational costs.

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Area of Science:

Signal Processing
Acoustics
Speech Technology

Background:

Speech transients are vital for speech sound identification and discrimination.
Previous methods for emphasizing speech transients are computationally intensive and not suitable for real-time applications.
Improving speech intelligibility in noisy environments remains a significant challenge.

Purpose of the Study:

To develop a computationally efficient method for identifying and emphasizing speech transients.
To enhance the intelligibility of speech, particularly in noisy conditions.
To enable real-time applications for speech processing.

Main Methods:

Utilized wavelet packet transform for subband decomposition.
Incorporated variable frame rate (VFR) analysis to define a transitivity function for wavelet coefficients.
Integrated unvoiced consonant detection to identify specific speech intervals for emphasis.

Main Results:

The proposed method effectively identifies and emphasizes speech transients.
The algorithm amplifies speech transients during unvoiced consonant intervals.
Preliminary implementation demonstrates improved computational efficiency compared to prior methods.

Conclusions:

The novel method offers an efficient way to process speech transients.
This technique has the potential to significantly improve speech intelligibility in real-time applications.
The combination of wavelet packet transform, VFR analysis, and unvoiced consonant detection provides a robust solution for speech enhancement.