7th International Conference on Spoken Language Processing

September 16-20, 2002
Denver, Colorado, USA

Evaluation of Formant-Like Features for ASR

Katrin Weber (1), Febe de Wet (2), Bert Cranen (2), Lou Boves (2), Samy Bengio (1), Hervé Bourlard (1)

(1) Dalle Molle Institute of Perceptual Artificial Intelligence, Switzerland; (2) University of Nijmegen, The Netherlands

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’, noise- robust, state-of-theart 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.


Full Paper

Bibliographic reference.  Weber, Katrin / Wet, Febe de / Cranen, Bert / Boves, Lou / Bengio, Samy / Bourlard, Hervé (2002): "Evaluation of formant-like features for ASR", In ICSLP-2002, 2101-2104.