Adaptive signal processing
Speech Communication - Special issue on speech processing in adverse conditions
The use of adaptive frame for speech recognition
EURASIP Journal on Applied Signal Processing
Comparison between Genetic Algorithms and Particle Swarm Optimization
EP '98 Proceedings of the 7th International Conference on Evolutionary Programming VII
Grammatical Swarm: The generation of programs by social programming
Natural Computing: an international journal
Speech spectral amplitude estimators using optimally shaped Gamma and Chi priors
Speech Communication
Expert Systems with Applications: An International Journal
A study of particle swarm optimization particle trajectories
Information Sciences: an International Journal
INTEGRATION OF A VOICE RECOGNITION SYSTEM IN A SOCIAL ROBOT
Cybernetics and Systems
Evaluation of Objective Quality Measures for Speech Enhancement
IEEE Transactions on Audio, Speech, and Language Processing
On the computation of all global minimizers through particle swarm optimization
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Fuzzy Systems
A robust adaptive speech enhancement system for vehicular applications
IEEE Transactions on Consumer Electronics
Efficient embedded speech recognition for very large vocabulary Mandarin car-navigation systems
IEEE Transactions on Consumer Electronics
Hi-index | 0.00 |
Commercial speech recognizers have made possible many speech control applications such as wheelchair, tone-phone, multifunctional robotic arms and remote controls, for the disabled and paraplegic. However, they have a limitation in common in that recognition errors are likely to be produced when background noise surrounds the spoken command, thereby creating potential dangers for the disabled if recognition errors exist in the control systems. In this paper, a hybrid noise suppression filter is proposed to interface with the commercial speech recognizers in order to enhance the recognition accuracy under variant noisy conditions. It intends to decrease the recognition errors when the commercial speech recognizers are working under a noisy environment. It is based on a sigmoid function which can effectively enhance noisy speech using simple computational operations, while a robust estimator based on an adaptive-network-based fuzzy inference system is used to determine the appropriate operational parameters for the sigmoid function in order to produce effective speech enhancement under variant noisy conditions. The proposed hybrid noise suppression filter has the following advantages for commercial speech recognizers: (i) it is not possible to tune the inbuilt parameters on the commercial speech recognizers in order to obtain better accuracy; (ii) existing noise suppression filters are too complicated to be implemented for real-time speech recognition; and (iii) existing sigmoid function based filters can operate only in a single-noisy condition, but not under varying noisy conditions. The performance of the hybrid noise suppression filter was evaluated by interfacing it with a commercial speech recognizer, commonly used in electronic products. Experimental results show that improvement in terms of recognition accuracy and computational time can be achieved by the hybrid noise suppression filter when the commercial recognizer is working under various noisy environments in factories.