Deep convolutional neural networks are being actively investigated in a wide range of speech and audio processing applications including speech recognition, audio event detection and computational paralinguistics, owing to their ability to reduce factors of variations, for learning from speech. However, studies have suggested to favor a certain type of convolutional operations when building a deep convolutional neural network for speech applications although there has been promising results using different types of convolutional operations. In this work, we study four types of convolutional operations on different input features for speech emotion recognition under noisy and clean conditions in order to derive a comprehensive understanding. Since affective behavioral information has been shown to reflect temporally varying of mental state and convolutional operation are applied locally in time, all deep neural networks share a deep recurrent sub-network architecture for further temporal modeling. We present detailed quantitative module-wise performance analysis to gain insights into information flows within the proposed architectures. In particular, we demonstrate the interplay of affective information and the other irrelevant information during the progression from one module to another. Finally we show that all of our deep neural networks provide state-of-the-art performance on the eNTERFACE'05 corpus.
Recently, proposed the CLDNN architecture for speech recognition based on the log-Mels @cite_52 and the raw waveform signal @cite_20 , in which both models have been shown to more competitive than a LSTM and a CNN model alone or combined. They also demonstrated that with a sufficient amount of training data (roughly @math hours), a CLDNN trained on the raw waveform signal can match the one trained on the log-Mels. Moreover, they found the raw waveform and the log-Mels in fact provide complementary information. Based on the CLDNN architecture, @cite_62 published a model using the raw waveform signal for continuous emotion tracking. @cite_41 trained a CLDNN model on the log-Mels for speech emotion recognition and quantitatively analyzed the difference in spectrally decorrelating power between the discrete cosine transformation and the convolutional operation. @cite_37 repeated the comparison between CNN, LSTM and CLDNN for speech emotion recognition using spectrograms. @cite_34 applied the CLDNN architecture to classifying depression based on the log-Mels and spectrograms. They employed the full-spectrum temporally convolutional operation on the log-Mels but the temporally-only convolutional operation on the spectrograms.
@cite_37 @cite_62 @cite_41 @cite_52 @cite_34 @cite_20
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Learning an acoustic model directly from the raw waveform has been an active area of research. However, waveformbased models have not yet matched the performance of logmel trained neural networks. We will show that raw waveform features match the performance of log-mel filterbank energies when used with a state-of-the-art CLDNN acoustic model trained on over 2,000 hours of speech. Specifically, we will show the benefit of the CLDNN, namely the time convolution layer in reducing temporal variations, the frequency convolution layer for preserving locality and reducing frequency variations, as well as the LSTM layers for temporal modeling. In addition, by stacking raw waveform features with log-mel features, we achieve a 3 relative reduction in word error rate. Standard deep neural network-based acoustic models for automatic speech recognition (ASR) rely on hand-engineered input features, typically log-mel filterbank magnitudes. In this paper, we describe a convolutional neural network - deep neural network (CNN-DNN) acoustic model which takes raw multichannel waveforms as input, i.e. without any preceding feature extraction, and learns a similar feature representation through supervised training. By operating directly in the time domain, the network is able to take advantage of the signal's fine time structure that is discarded when computing filterbank magnitude features. This structure is especially useful when analyzing multichannel inputs, where timing differences between input channels can be used to localize a signal in space. The first convolutional layer of the proposed model naturally learns a filterbank that is selective in both frequency and direction of arrival, i.e. a bank of bandpass beamformers with an auditory-like frequency scale. When trained on data corrupted with noise coming from different spatial locations, the network learns to filter them out by steering nulls in the directions corresponding to the noise sources. Experiments on a simulated multichannel dataset show that the proposed acoustic model outperforms a DNN that uses log-mel filterbank magnitude features under noisy and reverberant conditions. Both Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) have shown improvements over Deep Neural Networks (DNNs) across a wide variety of speech recognition tasks. CNNs, LSTMs and DNNs are complementary in their modeling capabilities, as CNNs are good at reducing frequency variations, LSTMs are good at temporal modeling, and DNNs are appropriate for mapping features to a more separable space. In this paper, we take advantage of the complementarity of CNNs, LSTMs and DNNs by combining them into one unified architecture. We explore the proposed architecture, which we call CLDNN, on a variety of large vocabulary tasks, varying from 200 to 2,000 hours. We find that the CLDNN provides a 46 relative improvement in WER over an LSTM, the strongest of the three individual models. We train a bank of complex filters that operates on the raw waveform and is fed into a convolutional neural network for end-to-end phone recognition. These time-domain filterbanks (TD-filterbanks) are initialized as an approximation of mel-filterbanks, and then fine-tuned jointly with the remaining convolutional architecture. We perform phone recognition experiments on TIMIT and show that for several architectures, models trained on TD- filterbanks consistently outperform their counterparts trained on comparable mel-filterbanks. We get our best performance by learning all front-end steps, from pre-emphasis up to averaging. Finally, we observe that the filters at convergence have an asymmetric impulse response, and that some of them remain almost analytic. We investigate the effectiveness of generative adversarial networks (GANs) for speech enhancement, in the context of improving noise robustness of automatic speech recognition (ASR) systems. Prior work demonstrates that GANs can effectively suppress additive noise in raw waveform speech signals, improving perceptual quality metrics; however this technique was not justified in the context of ASR. In this work, we conduct a detailed study to measure the effectiveness of GANs in enhancing speech contaminated by both additive and reverberant noise. Motivated by recent advances in image processing, we propose operating GANs on log-Mel filterbank spectra instead of waveforms, which requires less computation and is more robust to reverberant noise. While GAN enhancement improves the performance of a clean-trained ASR system on noisy speech, it falls short of the performance achieved by conventional multi-style training (MTR). By appending the GAN-enhanced features to the noisy inputs and retraining, we achieve a 7 WER improvement relative to the MTR system. Environmental sound classification (ESC) is usually conducted based on handcrafted features such as the log-mel feature. Meanwhile, end-to-end classification systems perform feature extraction jointly with classification and have achieved success particularly in image classification. In the same manner, if environmental sounds could be directly learned from the raw waveforms, we would be able to extract a new feature effective for classification that could not have been designed by humans, and this new feature could improve the classification performance. In this paper, we propose a novel end-to-end ESC system using a convolutional neural network (CNN). The classification accuracy of our system on ESC-50 is 5.1 higher than that achieved when using logmel-CNN with the static log-mel feature. Moreover, we achieve a 6.5 improvement in classification accuracy over the state-of-the-art logmel-CNN with the static and delta log-mel feature, simply by combining our system and logmel-CNN.