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Vowel-onset detection by vowel-strength measurement, cochlear-nucleus simulation, and multilayer perceptrons
R W Kortekaas1, D J Hermes, G F Meyer
1Institute for Perception Research/IPO, Eindhoven, The Netherlands.
The Journal of the Acoustical Society of America
|February 1, 1996
Summary
This study improved vowel onset detection algorithms for speech processing. A multilayer perceptron network achieved performance comparable to the original algorithm, enhancing general applicability.
Area of Science:
- Speech processing
- Acoustics
- Machine learning
Background:
- The Hermes algorithm for vowel onset detection performed well on fluent speech but failed for speech by deaf individuals.
- Modifications were needed for well-articulated speech, indicating limitations in general applicability.
Purpose of the Study:
- To improve the general applicability of the Hermes vowel onset detection algorithm.
- To explore alternative strategies for more robust vowel onset detection.
Main Methods:
- Simulated transient-chopper responses in the cochlear nucleus.
- Trained multilayer perceptrons (MLPs) on mel-scaled spectra.
- Compared algorithm performance on large read speech databases.
Main Results:
- The cochlear-nucleus simulation strategy showed a higher false-alarm rate and was level-dependent.
- The MLP network achieved performance comparable to the original Hermes algorithm.
- Temporal intensity and spectral envelope information are crucial for vowel onset detection.
Conclusions:
- Multilayer perceptrons offer a viable alternative for vowel onset detection, comparable to existing methods.
- Harmonicity information can refine detection by filtering unvoiced onsets.
- Improved algorithms enhance the potential for applications in speech processing for diverse populations.
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