Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Techniques for the regeneration of wideband speech from narrowband speech
EURASIP Journal on Applied Signal Processing - Nonlinear signal and image processing - part I
Discrete Time Processing of Speech Signals
Discrete Time Processing of Speech Signals
Speaker-specific mapping for text-independent speaker recognition
Speech Communication
Artificial Neural Networks
Frequency recovery of narrow-band speech using adaptive spline neural networks
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 02
| Hi-index | 0.00 |
Speech recorded from a throat microphone is robust to the surrounding noise, but sounds unnatural unlike the speech recorded from a close-speaking microphone. This paper addresses the issue of improving the perceptual quality of the throat microphone speech by mapping the speech spectra from the throat microphone to the close-speaking microphone. A neural network model is used to capture the speaker-dependent functional relationship between the feature vectors (cepstral coefficients) of the two speech signals. A method is proposed to ensure the stability of the all-pole synthesis filter. Objective evaluations indicate the effectiveness of the proposed mapping scheme. The advantage of this method is that the model gives a smooth estimate of the spectra of the close-speaking microphone speech. No distortions are perceived in the reconstructed speech. This mapping technique is also used for bandwidth extension of telephone speech.