Eiko Yamada

Feature extraction and normalization for speech recognition

Speech data is converted into logarithmic spectrum data and orthogonally transformed to develop feature vectors. Normalization coefficient data and unit vector data are stored. An inner product of the feature vector data and the unit vector data is calculated. The inner product may be the average of inner products for a word or a sentence, or may be a regressive average of them. A normalization vector, which corresponds to a second or higher order curve obtained by least-square error approximation of the speech data on logarithmic spectrum space, is calculated on the transformed feature vector space by using the inner product, the normalization coefficient data, and the unit vector data. Normalization of the feature vectors is performed by subtracting the normalization vector from the feature vectors on the transformed feature vector space. Then, a recognition is performed based on the normalized feature vector.

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.