Capturing fine-phonetic variation in speech through automatic classification of articulatory features
Scharenborg, O., Wan, V., & Moore, R. K.
Capturing fine-phonetic variation in speech through automatic classification of articulatory features. In Speech Recognition and Intrinsic Variation Workshop [SRIV2006]
(pp. 77-82). ISCA Archive.
The ultimate goal of our research is to develop a computational
model of human speech recognition that is able to capture the
effects of fine-grained acoustic variation on speech recognition
behaviour. As part of this work we are investigating automatic
feature classifiers that are able to create reliable and accurate
transcriptions of the articulatory behaviour encoded in the acoustic
speech signal. In the experiments reported here, we compared
support vector machines (SVMs) with multilayer perceptrons
(MLPs). MLPs have been widely (and rather successfully) used
for the task of multi-value articulatory feature classification,
while (to the best of our knowledge) SVMs have not. This paper
compares the performances of the two classifiers and analyses the
results in order to better understand the articulatory representations.
It was found that the MLPs outperformed the SVMs, but it
is concluded that both classifiers exhibit similar behaviour in
terms of patterns of errors.