Employing voice-based emotion recognition function in artificial intelligence (AI) product will improve the user experience. Most of researches that have been done only focus on the speech collected under controlled conditions. The scenarios evaluated in these research were well controlled. The conventional approach may fail when background noise or nonspeech filler exist. In this paper, we propose an ensemble framework combining several aspects of features from audio. The framework incorporates gender and speaker information relying on multi-task learning. Therefore it is able to dig and capture emotional information as much as possible. This framework is evaluated on multimodal emotion challenge (MEC) 2017 corpus which is close to real world. The proposed framework outperformed the best baseline system by 29.5 (relative improvement).
Deep learning techniques were emerging as new classifier in speech related machine learning area @cite_9 . The deep learning techniques, such as (DNN), (CNN) and (RNN) is able to model the feature in a high dimensional manifold space. DNN as static classifier and RNN as dynamic classifier showed their advantage in emotion recognition task compared with conventional approaches @cite_18 . Especially, @cite_18 used attention based weighted pooling to extract acoustic representation. It showed advantage over the conventional hand crafted features. Multi-task learning recently raised as a technique helping train better model for main task @cite_1 @cite_6 . However these work used valence and arousal as auxiliary tasks which may be difficult to get.
@cite_1 @cite_9 @cite_18 @cite_6
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With rapid developments in the design of deep architecture models and learning algorithms, methods referred to as deep learning have come to be widely used in a variety of research areas such as pattern recognition, classification, and signal processing. Deep learning methods are being applied in various recognition tasks such as image, speech, and music recognition. Convolutional Neural Networks (CNNs) especially show remarkable recognition performance for computer vision tasks. In addition, Recurrent Neural Networks (RNNs) show considerable success in many sequential data processing tasks. In this study, we investigate the result of the Speech Emotion Recognition (SER) algorithm based on CNNs and RNNs trained using an emotional speech database. The main goal of our work is to propose a SER method based on concatenated CNNs and RNNs without using any traditional hand-crafted features. By applying the proposed methods to an emotional speech database, the classification result was verified to have better accuracy than that achieved using conventional classification methods. This paper proposes an effective segmentation-free approach using a hybrid neural network hidden Markov model (NN-HMM) for offline handwritten Chinese text recognition (HCTR). In the general Bayesian framework, the handwritten Chinese text line is sequentially modeled by HMMs with each representing one character class, while the NN-based classifier is adopted to calculate the posterior probability of all HMM states. The key issues in feature extraction, character modeling, and language modeling are comprehensively investigated to show the effectiveness of NN-HMM framework for offline HCTR. First, a conventional deep neural network (DNN) architecture is studied with a well-designed feature extractor. As for the training procedure, the label refinement using forced alignment and the sequence training can yield significant gains on top of the frame-level cross-entropy criterion. Second, a deep convolutional neural network (DCNN) with automatically learned discriminative features demonstrates its superiority to DNN in the HMM framework. Moreover, to solve the challenging problem of distinguishing quite confusing classes due to the large vocabulary of Chinese characters, NN-based classifier should output 19900 HMM states as the classification units via a high-resolution modeling within each character. On the ICDAR 2013 competition task of CASIA-HWDB database, DNN-HMM yields a promising character error rate (CER) of 5.24 by making a good trade-off between the computational complexity and recognition accuracy. To the best of our knowledge, DCNN-HMM can achieve a best published CER of 3.53 . Speech emotion recognition is challenging because of the affective gap between the subjective emotions and low-level features. Integrating multilevel feature learning and model training, deep convolutional neural networks (DCNN) has exhibited remarkable success in bridging the semantic gap in visual tasks like image classification, object detection. This paper explores how to utilize a DCNN to bridge the affective gap in speech signals. To this end, we first extract three channels of log Mel-spectrograms (static, delta, and delta delta) similar to the red, green, blue (RGB) image representation as the DCNN input. Then, the AlexNet DCNN model pretrained on the large ImageNet dataset is employed to learn high-level feature representations on each segment divided from an utterance. The learned segment-level features are aggregated by a discriminant temporal pyramid matching (DTPM) strategy. DTPM combines temporal pyramid matching and optimal Lp-norm pooling to form a global utterance-level feature representation, followed by the linear support vector machines for emotion classification. Experimental results on four public datasets, that is, EMO-DB, RML, eNTERFACE05, and BAUM-1s, show the promising performance of our DCNN model and the DTPM strategy. Another interesting finding is that the DCNN model pretrained for image applications performs reasonably good in affective speech feature extraction. Further fine tuning on the target emotional speech datasets substantially promotes recognition performance. Deep convolutional neural networks (CNN) have seen tremendous success in large-scale generic object recognition. In comparison with generic object recognition, fine-grained image classification (FGIC) is much more challenging because (i) fine-grained labeled data is much more expensive to acquire (usually requiring domain expertise); (ii) there exists large intra-class and small inter-class variance. Most recent work exploiting deep CNN for image recognition with small training data adopts a simple strategy: pre-train a deep CNN on a large-scale external dataset (e.g., ImageNet) and fine-tune on the small-scale target data to fit the specific classification task. In this paper, beyond the fine-tuning strategy, we propose a systematic framework of learning a deep CNN that addresses the challenges from two new perspectives: (i) identifying easily annotated hyper-classes inherent in the fine-grained data and acquiring a large number of hyper-class-labeled images from readily available external sources (e.g., image search engines), and formulating the problem into multitask learning; (ii) a novel learning model by exploiting a regularization between the fine-grained recognition model and the hyper-class recognition model. We demonstrate the success of the proposed framework on two small-scale fine-grained datasets (Stanford Dogs and Stanford Cars) and on a large-scale car dataset that we collected.