1 research outputs found
Sonority Measurement Using System, Source, and Suprasegmental Information
Sonorant sounds are characterized by regions with prominent formant
structure, high energy and high degree of periodicity. In this work, the
vocal-tract system, excitation source and suprasegmental features derived from
the speech signal are analyzed to measure the sonority information present in
each of them. Vocal-tract system information is extracted from the Hilbert
envelope of numerator of group delay function. It is derived from zero time
windowed speech signal that provides better resolution of the formants. A
five-dimensional feature set is computed from the estimated formants to measure
the prominence of the spectral peaks. A feature representing strength of
excitation is derived from the Hilbert envelope of linear prediction residual,
which represents the source information. Correlation of speech over ten
consecutive pitch periods is used as the suprasegmental feature representing
periodicity information. The combination of evidences from the three different
aspects of speech provides better discrimination among different sonorant
classes, compared to the baseline MFCC features. The usefulness of the proposed
sonority feature is demonstrated in the tasks of phoneme recognition and
sonorant classification