Abstract
Summary form only given, as follows. An automatic segmentation and recognition system based on a neural network model is proposed, and a backpropagation learning algorithm is employed to establish the networks. The main new idea for segmentation is to classify the energy, spectrum, and pitch-period transitions that occur at the boundaries between syllables. These feature transitions are used as the input patterns of the neural network . The segmented syllables are then used as the basic units for recognition by being fed them into the well-trained network. Ten digits (0-9) and syllables spoken in Mandarin are used in the speaker-independent phase for segmentation experiments, and only ten digits are used in the speaker-dependent phase for recognition experiments. With an average speaking rate of 150 digits per minute, a segmentation accuracy with a coincidence rate of 95.7% and recognition rate of 97.2% can be achieved.
Original language | English |
---|---|
Number of pages | 1 |
Publication status | Published - 1990 Dec 1 |
Event | 1990 IEEE International Symposium on Information Theory - San Diego, CA, USA Duration: 1990 Jan 14 → 1990 Jan 19 |
Other
Other | 1990 IEEE International Symposium on Information Theory |
---|---|
City | San Diego, CA, USA |
Period | 90-01-14 → 90-01-19 |
All Science Journal Classification (ASJC) codes
- Engineering(all)