We propose speaker clustering methods for speech recognition based on vocal tract (VT) size related articulatory parameters associated with individual speakers. Two parameters characterizing gross VT dimensions are first derived from the formant frequencies of two vowels and are then used to cluster speakers. The resulting speaker clusters are significantly different from speaker clusters obtained by conventional acoustic criteria. Then phoneme recognition experiments are carried out by using speaker-clustered HMMs (SC-HMMs) trained for each cluster. The proposed method requires a small amount of speech data for speaker clustering and for selecting the most suitable SC-HMM for a target speaker, but gives higher recognition rates than conventional speaker clustering methods based on acoustic criteria.
ASJC Scopus subject areas
- Modelling and Simulation
- Language and Linguistics
- Linguistics and Language
- Computer Vision and Pattern Recognition
- Computer Science Applications