Incorporating Noise Robustness in Speech Command Recognition by Noise Augmentation of Training Data

The advent of new devices, technology, machine learning techniques, and the availability of free large speech corpora results in rapid and accurate speech recognition. In the last two decades, extensive research has been initiated by researchers and different organizations to experiment with new tec...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Switzerland), 2020-04, Vol.20 (8), p.2326
Main Authors: Pervaiz, Ayesha, Hussain, Fawad, Israr, Huma, Tahir, Muhammad Ali, Raja, Fawad Riasat, Baloch, Naveed Khan, Ishmanov, Farruh, Zikria, Yousaf Bin
Format: Article
Language:English
Subjects:
Accuracy
acoustic modelling
Acoustics
Algorithms
automatic speech recognition
Automation
Data science
Datasets
Deep Learning
deep neural networks
Human-computer interface
Humans
Internet of Things
language modelling
Literature reviews
Machine Learning
Neural networks
Neural Networks, Computer
Noise
Smart houses
Speech
Speech Perception - physiology
Speech processing
Speech recognition
Speech Recognition Software
Voice recognition
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Summary:The advent of new devices, technology, machine learning techniques, and the availability of free large speech corpora results in rapid and accurate speech recognition. In the last two decades, extensive research has been initiated by researchers and different organizations to experiment with new techniques and their applications in speech processing systems. There are several speech command based applications in the area of robotics, IoT, ubiquitous computing, and different human-computer interfaces. Various researchers have worked on enhancing the efficiency of speech command based systems and used the speech command dataset. However, none of them catered to noise in the same. Noise is one of the major challenges in any speech recognition system, as real-time noise is a very versatile and unavoidable factor that affects the performance of speech recognition systems, particularly those that have not learned the noise efficiently. We thoroughly analyse the latest trends in speech recognition and evaluate the speech command dataset on different machine learning based and deep learning based techniques. A novel technique is proposed for noise robustness by augmenting noise in training data. Our proposed technique is tested on clean and noisy data along with locally generated data and achieves much better results than existing state-of-the-art techniques, thus setting a new benchmark.
ISSN:1424-8220
1424-8220
DOI:10.3390/s20082326