Abstract
This work presents a novel means of extracting fixedlength parameters from voice signals, such that words can be recognized in linear time. The power and the zero crossing rate are first calculated segment by segment from a voice signal; by doing so, two feature sequences are generated. We then construct an FIR system across these two sequences. The parameters of this FIR system, used as the input of a multilayer proceptron recognizer, can be derived by recursive LSE (least-square estimation), implying that the complexity of overall process is linear to the signal size. In the second part of this work, we introduce a weighting factor λ to emphasize recent input; therefore, we can further recognize continuous speech signals. Experiments employ the voice signals of numbers, from zero to nine, spoken in Mandarin Chinese. The proposed method is verified to recognize voice signals efficiently and accurately.
| Original language | English |
|---|---|
| Pages (from-to) | 464-467 |
| Number of pages | 4 |
| Journal | World Academy of Science, Engineering and Technology |
| Volume | 77 |
| State | Published - 05 2011 |
| Event | International Conference on Digital Signal Processing (ICDSP 2011) - Paris, France Duration: 24 06 2011 → 26 06 2011 |
Keywords
- FIR system
- Multilayer perceptron
- Recursive LSE
- Speech recognition