Speech recognition using sub-word units dependent on phonetic contexts of both training and recognition vocabularies

Proposes a new speech recognition algorithm using a new context-dependent recognition unit design method for efficient and precise acoustic modeling. This algorithm uses both training and recognition vocabularies to select context-dependent units which precisely represent acoustic variations due to phonetic contexts in a recognition vocabulary. An efficient training algorithm for selected context-dependent units is also proposed. In speaker-independent isolated-word recognition experiments, the proposed algorithm gave a 11% error reduction for 5000-word recognition, and gave a 43% error reduction for 10-digit recognition. These results confirmed the effectiveness of the proposed method.