Large-vocabulary isolated word recognition with fast coarse time alignment

An isolated word recognition system for large vocabularies (1000 words and up) with an average recognition rate of more than 98 per cent is presented. Each utterance is characterized by a sequence of feature vectors which are obtained by an autocorrelation analysis. The resulting coefficients are quantized in such a way, that an entire feature vector can be stored in a single data word. A distance measure adapted to this representation has been developed. The classification is performed hierarchically in two steps. In the preselection stage, the words are divided into 16 segments and hardware is employed for a coarse nonlinear mapping. A short ranked list of candidates is processed by the following final classifier which performs a nonlinear time alignment of the fully resolved patterns using Dynamic Programming. Thus a short response time and a high recognition performance of the system are achieved. Without full use of parallelism an overall response time for the large vocabulary of less than one second is obtained on a signal processor.