Abstract
Laryngealizations are irregular voiced portions of speech, which can have morpho-syntactic functions and can disturb the automatic computation of FO. Two methods for the automatic detection of laryngealizations are described in this paper: With a Gaussian classifier using spectral and cepstral features a recognition rate of 80% (false alarm rate of 8%) could be achieved. As an alternative a “non-standard” method has been developed: an artificial neural network (ANN) was used for non-linear inverse filtering of speech signals. The inversely filtered signal was directly used as input for another ANN, which was trained to detect laryngealizations. In preliminary experiments we obtained a recognition rate of 65% (12% false alarms).
This work was supported by the German Ministry for Research and Technology (BMFT) in the joint research project ASL/VERBMOBIL and by the Deutsche Forschungsgemeinschaft (DFG). Only the authors are responsible for the contents.
Experiments with learn≠test (speaker-independent, leave-one-out), showed the same tendencies.
Voice source signals (figure 49.1) were measured with a laryngograph.
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Kießling, A., Kompe, R., Niemann, H., Nöth, E., Batliner, A. (1995). Voice Source State as a Source of Information in Speech Recognition: Detection of Laryngealizations. In: Ayuso, A.J.R., Soler, J.M.L. (eds) Speech Recognition and Coding. NATO ASI Series, vol 147. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-57745-1_49
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