Speech recognition in noisy environments: a survey
Speech Communication
Robust automatic speech recognition with missing and unreliable acoustic data
Speech Communication
Artificial Neural Networks
Signal processing for in-car communication systems
Signal Processing
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Invited paper: Automatic speech recognition: History, methods and challenges
Pattern Recognition
Neural Computing and Applications
Speech recognition with artificial neural networks
Digital Signal Processing
Multiple-view multiple-learner active learning
Pattern Recognition
On the recognition of cochlear implant-like spectrally reduced speech with MFCC and HMM-based ASR
IEEE Transactions on Audio, Speech, and Language Processing
Sparse imputation for large vocabulary noise robust ASR
Computer Speech and Language
Expert Systems with Applications: An International Journal
The use of phase in complex spectrum subtraction for robust speech recognition
Computer Speech and Language
An automated framework for software test oracle
Information and Software Technology
Multiple-View Multiple-Learner Semi-Supervised Learning
Neural Processing Letters
Artificial neural networks as multi-networks automated test oracle
Automated Software Engineering
IEEE Transactions on Audio, Speech, and Language Processing
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Blind source extraction for robust speech recognition in multisource noisy environments
Computer Speech and Language
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Automatic Speech Recognition (ASR) is a technology for identifying uttered word(s) represented as an acoustic signal. However, one of the important aspects of a noise-robust ASR system is its ability to recognise speech accurately in noisy conditions. This paper studies the applications of Multi-Nets Artificial Neural Networks (M-N ANNs), a realisation of multiple-views multiple-learners approach, as Multi-Networks Speech Recognisers (M-NSRs) in providing a real-time, frequency-based noise-robust ASR model. M-NSRs define speech features associated with each word as a different view and apply a standalone ANN as one of the learners to approximate that view; meanwhile, multiple-views single-learner (MVSL) ANN-based speech recognisers employ only one ANN to memorise the features of the entire vocabulary. In this research, an M-NSR was provided and evaluated using unforeseen test data that were affected by white, brown, and pink noises; more specifically, 27 experiments were conducted on noisy speech to measure the accuracy and recognition rate of the proposed model. Furthermore, the results of the M-NSR were compared in detail with an MVSL ANN-based ASR system. The M-NSR recorded an improved average recognition rate by up to 20.14% when it was given the test data infected with noise in our experiments. It is shown that the M-NSR with higher degree of generalisability can handle frequency-based noise because it has higher recognition rate than the previous model under noisy conditions.