PEMO: Speech Recognition
with Perceptive Feature Extraction

A computational model of the auditory periphery (PEMO) was developed by the Medical Physics Group at Oldenburg University. PEMO was originally developed to simulate psychoacoustical experiments like temporal or spectral masking experiments. Recently, the model was applied to different topics in speech processing like speech intelligibility prediction, objective speech quality measurement and automatic speech recognition (ASR).
The motivation for our work in the field of ASR is that the human auditory system can be regarded as a very robust "speech regognition system" which allows us to understand speech in very noisy environments. Today's ASR systems, on the other hand, usually perform quite bad even in low noise. Simulating the "internal representation" of speech with an auditory-based feature extraction like PEMO should allow a more robust automatic recognition of speech.

Processing Stages of PEMO

The representation of speech and sounds after PEMO-processing:

Recognition experiments

were performed with PEMO feature extraction in a range of different setups. The robustness of the auditory-based preprocessing was compared with other front ends, with both HMM and neural network recognizers. The effect of additional monaural and binaural noise suppression prior to feature extraction was investigated. Current research focusses on sub word unit recognition in noise. Have a look at the below mentionend papers, if you're interested, or contact Michael Kleinschmidt, Christine Hartmann, or Jürgen Tchorz.

Related Papers and Articles:

M. Kleinschmidt, M. Marzinzik und B. Kollmeier:
Combining Monaural Noise Reduction Algorithms and Perceptive Preprocessing for Robust Speech Recognition. To appear in: "Psychophysics, Physiology, and Models of Hearing", edited by T. Dau, V. Hohmann, and B. Kollmeier. World Scientific, Singapore (1999)
Download (pdf, 180 kbyte)

J. Tchorz and B. Kollmeier:

A model of auditory perception as front end for automatic speech recognition. J. Acoust. Soc. Am. (JASA), 1999 (accepted)

J. Tchorz and B. Kollmeier:

A psychoacoustical model of the auditory periphery as front end for ASR. Proc. ASA/EAA/DEGA Joint Meeting on Acoustics, March 1999, Berlin, Germany (in press)
Download (pdf, 60 kbyte)

J. Tchorz, M. Kleinschmidt, K. Kasper and B. Kollmeier:

Auditory Feature Extraction and Recognizer Dependencies. Paper presented at "Workshop on Robust Methods for Speech Recognition", May 25-26, 1999, Tampere, Finland, pp. 67-70
Download (pdf, 190 kbyte)

M. Kleinschmidt:

Störgeräuschunterdrueckung und gehörgerechte Vorverarbeitung für die automatische Spracherkennung. Master's Thesis (Diplomarbeit), 1998.
Download (pdf, 1.6 mbyte)

M. Kleinschmidt, J. Tchorz, T. Wittkop, V. Hohmann, and B. Kollmeier:

Robuste Spracherkennung durch binaurale Richtungsfilterung und
gehörgerechte Vorverarbeitung

"Fortschritte der Akustik - DAGA 1998, Zürich"
Download (gzipped postscript, 70k)

J. Tchorz, K. Kasper, H. Reininger, and B. Kollmeier:

On the Interplay between auditory-based features and locally recurrent neural networks for robust speech recognition in noise
Eurospeech ´97 , p. 2075-2078, ESCA, Patras, Greece, 1997.
Download (postscript, 392k)

J. Tchorz, M. Wesselkamp, and B. Kollmeier:

Gehörgerechte Merkmalsextraktion zur robusten Spracherkennung in Störgeräuschen
Fortschritte der Akustik - DAGA 96, p. 532-533, DEGA, Oldenburg, 1996.
Download (postscript, 81k)

T. Dau, D. Püschel, and A. Kohlrausch:

A quantitative model of the ``effective'' signal processing in the auditory system: I. Model
J. Acoust. Soc. Am., vol. 99, p. 3633-3631, 1996

K. Kasper, H. Reininger, and D. Wolf:

Exploiting the Potential of Auditory Preprocessing for Robust Speech Recognition by Locally Recurrent Neural Networks
Proc. Int. Conf. Acoustics, Speech and Signal Processing (ICASSP), vol. 2 , p. 1223-1227, 1997

See also the publication list of our group.

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Last modified: July 15, 1998