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In-ear microphone speech data segmentation and recognition using neural networks

In-ear microphone speech data segmentation and recognition using neural networks
In-ear microphone speech data segmentation and recognition using neural networks
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
1424435343
262-267
Institute of Electrical and Electronics Engineers
Bulbuller, G.
104bb958-84de-488b-b993-02286979245d
Fargues, M.P.
9c73867d-327e-461f-8d6f-afdd0d639020
Vaidyanathan, R.
f062a7b1-fc7e-4227-9e1b-ca0b61330237
Bulbuller, G.
104bb958-84de-488b-b993-02286979245d
Fargues, M.P.
9c73867d-327e-461f-8d6f-afdd0d639020
Vaidyanathan, R.
f062a7b1-fc7e-4227-9e1b-ca0b61330237

Bulbuller, G., Fargues, M.P. and Vaidyanathan, R. (2006) In-ear microphone speech data segmentation and recognition using neural networks. In Proceedings of the IEE 12th Digital Signal Processing Workshop, 4th Signal Processing Education Workshop. Institute of Electrical and Electronics Engineers. pp. 262-267 . (doi:10.1109/DSPWS.2006.265387).

Record type: Conference or Workshop Item (Paper)

Abstract

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

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More information

Published date: 2006
Venue - Dates: IEE 12th Digital Signal Processing Workshop, 4th Signal Processing Education Workshop, Wyoming, USA, September 24-27 2006, 2006-09-24 - 2006-09-27

Identifiers

Local EPrints ID: 45658
URI: https://eprints.soton.ac.uk/id/eprint/45658
ISBN: 1424435343
PURE UUID: cc6da12d-ffd6-4ab1-9c50-0c502bcc4964

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Date deposited: 16 Apr 2007
Last modified: 13 Mar 2019 21:05

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Contributors

Author: G. Bulbuller
Author: M.P. Fargues
Author: R. Vaidyanathan

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