On the use of acoustic-phonetic features in interactive labelling of multi-lingual speech corpora

Results are reported from research on the use of continuously valued acoustic-phonetic features in the multi-language label alignment of combined speech corpora from three European languages: Danish, English, and Italian. A self-organizing neural network is used to transform cepstrum coefficients into a set of features, which are subsequently transformed into a set of principal components. These are used to model individual phonemes, which are used in a Viterbi search/level-building process to align an independently given string of phonemes with the corresponding speech signal. The results obtained show an overall accuracy of 55.7% in the positioning of the label boundary transitions in the combined test corpus. Detailed analysis shows that certain sound class boundaries are very accurately positioned. To provide a general solution to the problem of positioning badly positioned boundaries, an interactive component of the alignment system has been developed. First results demonstrate this component to be very valuable in the task of user-assisted boundary positioning