Speaker variability is a major problem in today's state-of- the-art speech recognition systems. Parameterisation of speech in terms of Acoustic Parameters (APs) motivated by phonetic feature theory has shown to be more robustness to speaker variability as compared to cepstral coefficients when tested on the task of broad-class recognition [1]. Also APs has been successfully applied for identification of semivowels [2,3]. The aim of the present study is to investigate the use of APs for phoneme recognition. An extended set of features is used to distinguish between all phonemes in the TIMIT database and APs related to the extended feature set are found in literature. A separability measure is calculated to investigate the importance of the suggested APs for the separation of phonemes and feature classes. Results show that the APs that are the most important for separation of classes of phonetic features are also the most important for separation of phonemes classes. This indicates that phonemes can be recognised on the basis of phonetic features captured by the use of APs. However much work still needs to be done to understand and reliably extract all of the acoustic correlates of the phonetic features applied.
Cite as: Hansen, A.V. (1997) Acoustic parameters optimised for recognition of phonetic features. Proc. 5th European Conference on Speech Communication and Technology (Eurospeech 1997), 397-400, doi: 10.21437/Eurospeech.1997-148
@inproceedings{hansen97_eurospeech,
author={Anya Varnich Hansen},
title={{Acoustic parameters optimised for recognition of phonetic features}},
year=1997,
booktitle={Proc. 5th European Conference on Speech Communication and Technology (Eurospeech 1997)},
pages={397--400},
doi={10.21437/Eurospeech.1997-148}
}