CONF
weber-ar-02-04/IDIAP
Evaluation of Formant-Like Features for ASR
Weber, Katrin
de Wet, F.
Cranen, B.
Boves, Louis
Bengio, Samy
Bourlard, Hervé
EXTERNAL
https://publications.idiap.ch/attachments/reports/2002/rr02-04.pdf
PUBLIC
https://publications.idiap.ch/index.php/publications/showcite/weber-rr-02-04
Related documents
International Conference on Spoken Language Processing (ICSLP 2002)
2002
Denver, CO, USA
September 2002
2101-2104
IDIAP-rr 02-04
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.
REPORT
weber-rr-02-04/IDIAP
Evaluation of Formant-Like Features for ASR
Weber, Katrin
de Wet, F.
Cranen, B.
Boves, Louis
Bengio, Samy
Bourlard, Hervé
EXTERNAL
https://publications.idiap.ch/attachments/reports/2002/rr02-04.pdf
PUBLIC
Idiap-RR-04-2002
2002
IDIAP
Martigny, Switzerland
Published: ICSLP 2002, Denver
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.