Missing-data methods with cepstral data
IDIAP, Martigny, Switzerland
Noise robust ASR is achieved by the MD ASR approach, as developed at Sheffield,
through the exploitation of spectral redundancy, together with techniques for
detecting clean spectral data. While models based on data which has been
orthogonalised (via DCT or PCA) are more accurate, MD has until now been
confined to models based on spectral data. So the question arrises: can the
advantages of spectral redundancy and orthogonalised data be combined? A number
of ideas relating to this have been tested recently at IDIAP.
This is followed by a concise "Summary of work done at IDIAP" throughout the
- use localised MLPs to generate "data utility masks" for any data
representation. So far these have not led to improved recognition performance.
- propagate spectral intervals of uncertainty through the DCT to obtain
corresponding cepstral intervals. Result is hugh intervals on all cepstral
coeffs, except when no spectral data is missing in one frame.
- introduce redundancy into cepstral domain by appending features obtained
through separate application of DCT to top and bottom half of frequency range.
Still very large intervals, and no improvement over cepstral baseline.
- restrict cepstral intervals by restricting spectral intervals through
refinements relative to the "maximum assumption". Can greataly reduce cepstral
uncertainty, but becomes complex, and so far no improvement in cepstral
Last modified: Mon Jan 29 15:59:06 GMT 2001