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Evaluation of Formant-Like Features for ASR
Type of publication: Conference paper
Citation: weber-ar-02-04
Booktitle: International Conference on Spoken Language Processing (ICSLP 2002)
Year: 2002
Month: 9
Address: Denver, CO, USA
Note: IDIAP-rr 02-04
Crossref: weber-rr-02-04:
Abstract: 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-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.
Userfields: ipdmembership={speech}, language={English},
Projects Idiap
Authors Weber, Katrin
de Wet, F.
Cranen, B.
Boves, Louis
Bengio, Samy
Bourlard, Hervé
Added by: [UNK]
Total mark: 0
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