EUROSPEECH 2001 Scandinavia
Speech analysis applications are typically based on short-term spectral analysis of the speech signal. Feature extraction process outputs one feature vector per frame. The features are further processed by application-dependent techniques, such as hidden Markov models or vector quantization. Independent from the application, it is often desirable that the feature vectors form separable clusters in the feature space. In this work, we study whether data is really clustered in the feature space and, if so, what is the number of the clusters in typical speech data. We consider different forms of the widely used cepstral features. Keywords: Speech analysis, pattern recognition, short-term features, cluster analysis.
Bibliographic reference. Kinnunen, Tomi / Kärkkäinen, Ismo / Fränti, Pasi (2001): "Is speech data clustered? - statistical analysis of cepstral features", In EUROSPEECH-2001, 2627-2630.