September 22-25, 1997
Segment-based speech recognition systems have been proposed in recent years to overcome some of the deficiencies of the current state- of-the-art HMM based systems. In this paper, we present a segmental speech recogniser, where the speech trajectory segments are modelled using their mean, variance and shape. The shape is chosen from a codebook of global vector quantised trajectories, obtained from uniformly segmented training utterances. Experiments were done for a speaker dependent isolated word recognition application under different noise environments. The results have shown that this segment based approach outperforms HMM based speech recognition systems under similar test conditions. In adverse noise conditions, up to 34% error rate reduction was achieved.
Bibliographic reference. Hariharan, Ramalingam / Hakkinen, Juha / Laurila, Kari / Suontausta, Janne (1997): "Noise robust segment-based word recognition using vector quantisation", In EUROSPEECH-1997, 1207-1210.