EVALUATION OF FORMANT-LIKE FEATURES FOR ASR IDIAP-RR 02-04 EVALUATION OF FORMANT-LIKE FEATURES FOR ASR
This paper investigates possibilities to automatically find a low-dimensional, formant-related physical representation of the speech signal, which is suitable for automatic speech recognition (ASR). This aim is motivated by the fact that formants have been shown to be discriminant features for ASR. Combinations of automatically extracted formant-like features and ‘conventional’, noiserobust, state-of-the-art features (such as MFCCs including spectral subtraction and cepstral mean subtraction) have previously been shown to be more robust in adverse conditions than state-of-the-art features alone. However, it is not clear how these automatically extracted formant-like features behave in comparison with true formants. The purpose of this paper is to investigate two methods to automatically extract formant-like features, and to compare these features to hand-labeled formant tracks as well as to standard MFCCs in terms of their performance on a vowel classification task. Acknowledgements: We would like to thank Prof. James Hillenbrand for making the AEV database available to us and for his swift reply to all our enquiries. The development of the database was supported by a research grant from the American National Institutes of Health (NIDCD 1-R01-DC01661). Febe de Wet's visit to IDIAP was made possible by the I.B.M. Frye grant. Katrin Weber was funded through the Swiss National Science Foundation, project FN 2000-059169.99/1.