text: One of the challenges in Speech Emotion Recognition (SER) "in the wild" is the large mismatch between training and test data (e.g. speakers and tasks). In order to improve the generalisation capabilities of the emotion models, we propose to use Multi-Task Learning (MTL) and use gender and naturalness as auxiliary tasks in deep neural networks. This method was evaluated in within-corpus and various cross-corpus classification experiments that simulate conditions "in the wild". In comparison to Single-Task Learning (STL) based state of the art methods, we found that our MTL method proposed improved performance significantly. Particularly, models using both gender and naturalness achieved more gains than those using either gender or naturalness separately. This benefit was also found in the high-level representations of the feature space, obtained from our method proposed, where discriminative emotional clusters could be observed.
  @cite_22: There has been a lot of prior work on representation learning for speech recognition applications, but not much emphasis has been given to an investigation of effective representations of affect from speech, where the paralinguistic elements of speech are separated out from the verbal content. In this paper, we explore denoising autoencoders for learning paralinguistic attributes i.e. categorical and dimensional affective traits from speech. We show that the representations learnt by the bottleneck layer of the autoencoder are highly discriminative of activation intensity and at separating out negative valence (sadness and anger) from positive valence (happiness). We experiment with different input speech features (such as FFT and log-mel spectrograms with temporal context windows), and different autoencoder architectures (such as stacked and deep autoencoders). We also learn utterance specific representations by a combination of denoising autoencoders and BLSTM based recurrent autoencoders. Emotion classification is performed with the learnt temporal dynamic representations to evaluate the quality of the representations. Experiments on a well-established real-life speech dataset (IEMOCAP) show that the learnt representations are comparable to state of the art feature extractors (such as voice quality features and MFCCs) and are competitive with state-of-the-art approaches at emotion and dimensional affect recognition.
  @cite_1: The automatic recognition of spontaneous emotions from speech is a challenging task. On the one hand, acoustic features need to be robust enough to capture the emotional content for various styles of speaking, and while on the other, machine learning algorithms need to be insensitive to outliers while being able to model the context. Whereas the latter has been tackled by the use of Long Short-Term Memory (LSTM) networks, the former is still under very active investigations, even though more than a decade of research has provided a large set of acoustic descriptors. In this paper, we propose a solution to the problem of ‘context-aware’ emotional relevant feature extraction, by combining Convolutional Neural Networks (CNNs) with LSTM networks, in order to automatically learn the best representation of the speech signal directly from the raw time representation. In this novel work on the so-called end-to-end speech emotion recognition, we show that the use of the proposed topology significantly outperforms the traditional approaches based on signal processing techniques for the prediction of spontaneous and natural emotions on the RECOLA database.
  @cite_24: The success of machine learning algorithms generally depends on data representation, and we hypothesize that this is because different representations can entangle and hide more or less the different explanatory factors of variation behind the data. Although specific domain knowledge can be used to help design representations, learning with generic priors can also be used, and the quest for AI is motivating the design of more powerful representation-learning algorithms implementing such priors. This paper reviews recent work in the area of unsupervised feature learning and deep learning, covering advances in probabilistic models, autoencoders, manifold learning, and deep networks. This motivates longer term unanswered questions about the appropriate objectives for learning good representations, for computing representations (i.e., inference), and the geometrical connections between representation learning, density estimation, and manifold learning.
  @cite_0: This paper presents a speech emotion recognition system using a recurrent neural network (RNN) model trained by an efficient learning algorithm. The proposed system takes into account the long-range context effect and the uncertainty of emotional label expressions. To extract high-level representation of emotional states with regard to its temporal dynamics, a powerful learning method with a bidirectional long short-term memory (BLSTM) model is adopted. To overcome the uncertainty of emotional labels, such that all frames in the same utterance are mapped into the same emotional label, it is assumed that the label of each frame is regarded as a sequence of random variables. Then, the sequences are trained by the proposed learning algorithm. The weighted accuracy of the proposed emotion recognition system is improved up to 12 compared to the DNN-ELM based emotion recognition system used as a baseline.
  @cite_16: 
  @cite_25: Abstract Speech emotion recognition is a challenging problem partly be-cause it is unclear what features are effective for the task. Inthis paper we propose to utilize deep neural networks (DNNs)to extract high level features from raw data and show that theyare effective for speech emotion recognition. We ﬁrst producean emotion state probability distribution for each speech seg-ment using DNNs. We then construct utterance-level featuresfrom segment-level probability distributions. These utterance-level features are then fed into an extreme learning machine(ELM),aspecialsimpleandefﬁcientsingle-hidden-layer neuralnetwork, to identify utterance-level emotions. The experimen-tal results demonstrate that the proposed approach effectivelylearns emotional information from low-level features and leadsto 20 relative accuracy improvement compared to the state-of-the-art approaches. IndexTerms : Emotion recognition, Deep neural networks, Ex-treme learning machine 1. Introduction Despite the great progress made in artiﬁcial intelligence, weare still far from being able to naturally interact with machines,partly because machines do not understand our emotion states.Recently, speech emotion recognition, which aims to recognizeemotion states from speech signals, has been drawing increas-ing attention. Speech emotion recognition is a very challengingtask of which extracting effective emotional features is an openquestion [1, 2].Adeep neural network (DNN) is afeed-forward neural net-work that has more than one hidden layers between its inputsand outputs. It is capable of learning high-level representationfrom the raw features and effectively classifying data [3, 4].With sufﬁcient training data and appropriate training strategies,DNNs perform very well in many machine learning tasks (e.g.,speech recognition [5]).Feature analysis in emotion recognition is much less stud-ied than that in speech recognition. Most previous studies em-pirically chose features for emotion classiﬁcation. In this study,aDNNtakesasinputtheconventional acousticfeatureswithinaspeechsegmentandproducessegment-levelemotionstateprob-ability distributions, from which utterance-level features areconstructed and used to determine the utterance-level emotionstate. Since the segment-level outputs already provide consid-erable emotional information and the utterance-level classiﬁca-
summary: As deep learning has gained a lot of interest and showed promising results in various fields of automatic speech analysis @cite_16 , it is being actively investigated in the field of SER too. Especially, by using representation learning @cite_24 , there has been effort to extract unsupervised features which generalise emotional speech rather than engineered features (e.g. pitch) @cite_22 @cite_1 . @cite_25 @cite_0 proposed an intrinsic way to build a high-level representation of emotion using the engineered features. They extracted segment-level engineered features (e.g. Mel-Frequency Cepstral Coefficients (MFCC) and pitch) and modelled probabilities of emotional categories using Deep Neural Network (DNN) @cite_25 and Bi-directional LSTM (BLSTM) @cite_0 . Then, functionals of the probabilities were used to extract utterance-level features, denoted as high-level feature representation. Extreme Learning Machine (ELM) using the utterance-level features outperformed conventional approaches such as HMM, SVM, and BLSTM. More recently, @cite_22 showed that unsupervised representation learning has limitation in complex subsequent structures for affect compared to @cite_0 's approach. Therefore, we chose @cite_25 @cite_0 's architectures as baselines (single task learning) and compared it to our proposal and will investigate the effectiveness of MTL in various settings.