| Autor: |
Ravi Vaidyanathan, G. Bulbuller, Monique P. Fargues |
| Rok vydání: |
2006 |
| Předmět: |
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| Zdroj: |
2006 IEEE 12th Digital Signal Processing Workshop & 4th IEEE Signal Processing Education Workshop. |
| DOI: |
10.1109/dspws.2006.265387 |
| Popis: |
Speech collected through a microphone placed in front of the mouth has been the primary source of data collection for speech recognition. However, this set-up also picks up any ambient noise present at the same time. As a result, locations which may provide shielding from surrounding noise have also been considered. This study considers an ear-insert microphone which collects speech from the ear canal to take advantage of the ear canal noise shielding properties to operate in noisy environments. Speech segmentation is achieved using short-time signal magnitude and short-time energy-entropy features. Cepstral coefficients extracted from each segmented utterance are used as input features to a back-propagation neural network for the seven isolated word recognizer implemented. Results show that a backpropagation neural network configuration may be a viable choice for this recognition task and that the best average recognition rate (94.73%) is obtained with mel-frequency cepstral coefficients for a two-layer network. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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