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Non-linguistic Vocalization Recognition Based on Convolutional, Long Short-Term Memory, Deep Neural Networks


Non-linguistic Vocalization Recognition refers to the detection and classification of non-speech voice such as laughter, sneeze, cough, cry, screaming, etc. It could be seen as a subtask of Acoustic Event Detection (AED). Great progress has been made by previous research to increase the accuracy of AED. On the front end, multiple kinds of features such as Mel-Frequency Cepstral Coefficients (MFCCs), Gammatone Cepstral Coefficients (GTCCs) and many other hand-crafted features were explored. While on the back end, models or methods such as Gaussian Mixture Models (GMMs), Hidden Markov Models (HMMs), Bags-of-Audio-Words (BoAW), Support Vector Machine (SVM) and various types of neural networks were experimented.

Recent researches on Automatic Speech Recognition (ASR) and Acoustic Scene Classification (ASC) show the advantage of using Convolutional, Long Short-Term Memory, Deep Neural Networks (CLDNNs) on audio processing tasks. In this thesis, I am building a non-linguistic vocalization recognition system using CLDNNs. Log Mel-filterbank coefficients are adopted as input features and data augmentation methods such as random shifting and noise mixture are discussed. The built system is evaluated on a custom dataset collected from several resources and tested for real time application. The performance of CLDNNs for non-linguistic vocalization recognition is also compared with hybrid GMM-SVMs, Convolutional Neural Networks, Long Short-Term Memory and a fully connected Deep Neural Network trained on VGGish embeddings.

The results indicate that CLDNNs outperform the other models in classification precision and recall. Visualization of CLDNNs are presented to help understand the framework. The model is proved accurate and fast enough for real time applications.

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