抄録
The LVQ-HMM hybrid algorithm was one of the first algorithms proposed in a recent approach aiming to integrate a highly discriminative artificial neural network-based classifier with an HMM capable of representing temporal structure effectively. The high phoneme classification capability of LVQ-HMM has already been demonstrated. However, the performance of LVQ-HMM has been less striking in more difficult, large-scale speech recognition situations, making evaluation of the algorithm controversial and suggesting a more detailed investigation of the properties of the algorithm in such situations. This technical report is thus devoted to re-evaluation of the hybrid algo-rithm, evaluated for word and phrase recognition tasks. Specifically, recognition ex perimentsare conducted under rather difficult, speaker-independent and large-vocabu-lary conditions. Our recognizer uses a phoneme-based strategy; in particular, the predictive LR-parser is incorporated for efficient recognition. Experimental results alone are unfortunately insufficient to cease the controversy. However, possible contribu-tions and aspects of the algorithm needing further improvement are brought to light.
