Proceedings ArticleDOI
Recurrent convolutional neural network for speech processing
Yue Zhao,Xingyu Jin,Xiaolin Hu +2 more
- pp 5300-5304
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TLDR
A recently developed deep learning model, recurrent convolutional neural network (RCNN), is proposed to use for speech processing, which inherits some merits of recurrent neural networks (RNN) and convolutionals (CNN) and is competitive with previous methods in terms of accuracy and efficiency.Abstract:
Different neural networks have exhibited excellent performance on various speech processing tasks, and they usually have specific advantages and disadvantages. We propose to use a recently developed deep learning model, recurrent convolutional neural network (RCNN), for speech processing, which inherits some merits of recurrent neural network (RNN) and convolutional neural network (CNN). The core module can be viewed as a convolutional layer embedded with an RNN, which enables the model to capture both temporal and frequency dependance in the spectrogram of the speech in an efficient way. The model is tested on speech corpus TIMIT for phoneme recognition and IEMOCAP for emotion recognition. Experimental results show that the model is competitive with previous methods in terms of accuracy and efficiency.read more
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Journal ArticleDOI
A Survey on Deep Learning: Algorithms, Techniques, and Applications
Samira Pouyanfar,Saad Sadiq,Yilin Yan,Haiman Tian,Yudong Tao,Maria Presa Reyes,Mei-Ling Shyu,Shu-Ching Chen,S. Sitharama Iyengar +8 more
TL;DR: A comprehensive review of historical and recent state-of-the-art approaches in visual, audio, and text processing; social network analysis; and natural language processing is presented, followed by the in-depth analysis on pivoting and groundbreaking advances in deep learning applications.
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Speech Emotion Recognition Using Deep Learning Techniques: A Review
Ruhul Amin Khalil,Edward Jones,Mohammad Inayatullah Babar,Tariqullah Jan,Mohammad Haseeb Zafar,Thamer Alhussain +5 more
TL;DR: An overview of Deep Learning techniques is presented and some recent literature where these methods are utilized for speech-based emotion recognition is discussed, including databases used, emotions extracted, contributions made toward speech emotion recognition and limitations related to it.
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Gated recurrent convolution neural network for OCR
Jianfeng Wang,Xiaolin Hu +1 more
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Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning
Jyotibdha Acharya,Arindam Basu +1 more
TL;DR: A deep CNN-RNN model that classifies respiratory sounds based on Mel-spectrograms is proposed that is able to achieve state of the art score on the ICBHI’17 dataset and deep learning models are shown to successfully learn domain specific knowledge when pre-trained with breathing data and produce significantly superior performance compared to generalized models.
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Deep Neural Network for Respiratory Sound Classification in Wearable Devices Enabled by Patient Specific Model Tuning
Jyotibdha Acharya,Arindam Basu +1 more
TL;DR: In this article, a deep CNN-RNN model was proposed to classify respiratory sounds based on Mel-spectrograms and a local log quantization strategy for model weights to reduce the memory footprint for deployment in memory constrained systems such as wearable devices.
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