| Abstrakt: |
Speech recognition systems based on LPC features sets have been applied successfully to a number of speech recognition tasks including an airlines information system, a directory assistance system, a voice repertory dialer, and a connected digit recognizer. All these systems have been based on a recognition model, originally proposed by Itakura, with a fixed analysis parameter set. To get an appreciation for the robustness of this feature set, an experimental investigation was undertaken to vary each of the parameters of the analysis system, and to measure the effect of recognition accuracy. The parameters chosen for study included-N, the size of the analysis frame; L, the shift between frames; p the number of LPC poles; α, the preemphasis factor, and LP, the analog filter cutoff. Four talkers were used in this study. Each talker recited the vocabulary (the digits, the letters of the alphabet and three command words) seven times for training, and ten times for subsequent testing. The speech was stored in digital form at a sampling rate of 20 000 Hz. Speaker trained templates were obtained from the training data. The results of this study indicate that the recognition accuracy remained high over a wide range of variation of analysis parameters. [ABSTRACT FROM AUTHOR] |
