We present a rapid design methodology that combines automated hyper-parameter tuning with semi-supervised training to build highly accurate and robust models for voice commands classification. Proposed approach allows quick evaluation of network architectures to fit performance and power constraints of available hardware, while ensuring good hyper-parameter choices for each network in real-world scenarios. Leveraging the vast amount of unlabeled data with a student teacher based semi-supervised method, classification accuracy is improved from 84 to 94 in the validation set. For model optimization, we explore the hyper-parameter space through population based training and obtain an optimized model in the same time frame as it takes to train a single model.
Usually to improve the accuracy and robustness of a model an ensemble of models are used. In one such example @cite_3 , authors used an ensemble of many strong neural network models (VGG16, Xception, ResNet50, InceptionV2, InceptionV3) in order to achieve a high performing model on the Speech Commands Dataset @cite_7 . However, deployment of such a model with varying hardware requirements is unrealistic from both an inference and training perspective for practical applications.
@cite_7 @cite_3
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Describes an audio dataset of spoken words designed to help train and evaluate keyword spotting systems. Discusses why this task is an interesting challenge, and why it requires a specialized dataset that is different from conventional datasets used for automatic speech recognition of full sentences. Suggests a methodology for reproducible and comparable accuracy metrics for this task. Describes how the data was collected and verified, what it contains, previous versions and properties. Concludes by reporting baseline results of models trained on this dataset. Automatic classification of sound commands is becoming increasingly important, especially for mobile and embedded devices. Many of these devices contain both cameras and microphones, and companies that develop them would like to use the same technology for both of these classification tasks. One way of achieving this is to represent sound commands as images, and use convolutional neural networks when classifying images as well as sounds. In this paper we consider several approaches to the problem of sound classification that we applied in TensorFlow Speech Recognition Challenge organized by Google Brain team on the Kaggle platform. Here we show different representation of sounds (Wave frames, Spectrograms, Mel-Spectrograms, MFCCs) and apply several 1D and 2D convolutional neural networks in order to get the best performance. Our experiments show that we found appropriate sound representation and corresponding convolutional neural networks. As a result we achieved good classification accuracy that allowed us to finish the challenge on 8-th place among 1315 teams.