Real-Time Emotion Recognition Using Echo State Networks

Scherer, Stefan; Oubbati, Mohamed; Schwenker, Friedhelm; Palm, Günther

doi:10.1007/978-3-540-69369-7_22

Cited by 7 publications

(4 citation statements)

References 8 publications

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“…However, the time complexity of LSTM has been reported to to be more time consuming compared to the ESN (see Table 1). Limited studies have adopted ESN for detecting emotion from speech, Scherer et al [15] explores the use of ESN for real-time emotion recognition from speech signals. The direct use of time series features from speech signals and avoiding a need for features extraction with the ESN model are proposed by [16].…”

Section: Literature Reviewmentioning

confidence: 99%

Reservoir Computing With Truncated Normal Distribution for Speech Emotion Recognition

Ibrahim

Loo

2022

MJCS

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Speech is an effective, quick, and important way for communicating and exchanging complex information between humans. Emotions have always been a part of normal human conversation which makes the speech more attractive. Because of this major role of both speech and emotion, many researchers are inspired by studying Speech Emotion Recognition (SER) which still has plenty of challenges. In this study, we proposed a novel reservoir computing approach with the initialization of random connection weights for the input weight by the truncated normal distribution. Furthermore, Population-Based Training (PBT) is adopted to optimize the hyperparameters of the whole Echo State Network (ESN) model which have a significant impact on the model performance. The proposed model has adopted bidirectional reservoir input to increase the memorization capability, and Sparse Random Projection (SRP) was applied for dimensional reduction as a simple, unsupervised, and low complexity approach. The speaker-independent strategy was employed on EMODB and SAVEE datasets as an acted speech emotion dataset and Aibo as a non-acted dataset. The model achieved 84.8%, 65.95%, and 45.99% unweighted average recalls on the EMODB, SAVEE, and Aibo datasets respectively. The results show that the proposed model outperforms the recent state-of-the-art studies with a cheaper computational cost.

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Section: Literature Reviewmentioning

confidence: 99%

Reservoir Computing With Truncated Normal Distribution for Speech Emotion Recognition

Ibrahim

Loo

2022

MJCS

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“…In the field of computer vision (CV), ESN have been used in image processing, such as image segmentation [277,278,279,280,281], image restoration [204], facial expression recognition [203]. Many methods also could process radio audio data [282,113,261,283], like video traffic [284], video annotation [285], audio Classification [115], speech recognition [286,287] and emotion recognition [288,289,290,291]. Meanwhile, there are also many applications based on 3D data, like 3D motion pattern indexing [292,293,294], activity /gesture recognition [295,205,202,296] and human eye movements [297].…”

Section: Real-world Tasks Orientatedmentioning

confidence: 99%

A Review of Designs and Applications of Echo State Networks

Sun¹,

Song²,

Hong³

et al. 2020

Preprint

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Recurrent Neural Networks (RNNs) have demonstrated their outstanding ability in sequence tasks and have achieved state-of-the-art in wide range of applications, such as industrial, medical, economic and linguistic. Echo State Network (ESN) is simple type of RNNs and has emerged in the last decade as an alternative to gradient descent training based RNNs. ESN, with a strong theoretical ground, is practical, conceptually simple, easy to implement. It avoids non-converging and computationally expensive in the gradient descent methods. Since ESN was put forward in 2002, abundant existing works have promoted the progress of ESN, and the recently introduced Deep ESN model opened the way to uniting the merits of deep learning and ESNs. Besides, the combinations of ESNs with other machine learning models have also overperformed baselines in some applications. However, the apparent simplicity of ESNs can sometimes be deceptive and successfully applying ESNs needs some experience. Thus, in this paper, we categorize the ESN-based methods to basic ESNs, DeepESNs and combinations, then analyze them from the perspective of theoretical studies, network designs and specific applications. Finally, we discuss the challenges and opportunities of ESNs by summarizing the open questions and proposing possible future works.

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“…By being able to achieve this gives a head-start and unique competitive edge over other market participants. Moreover, these systems display erratic recently been successfully applied over differing applications in sentiment analysis [18,19], health care [20], adaptive control [21], robotics [22,23], speech recognition [24,25], financial forecasting [26,27], and numerous other time series prediction [28,29] scenarios.…”

Section: Introductionmentioning

confidence: 99%

A reservoir computing approach for forecasting and regenerating both dynamical and time-delay controlled financial system behavior

et al. 2021

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Significant research in reservoir computing over the past two decades has revived interest in recurrent neural networks. Owing to its ingrained capability of performing high-speed and low-cost computations this has become a panacea for multi-variate complex systems having non-linearity within their relationships. Modelling economic and financial trends has always been a challenging task owing to their volatile nature and no linear dependence on associated influencers. Prior studies aimed at effectively forecasting such financial systems, but, always left a visible room for optimization in terms of cost, speed and modelling complexities. Our work employs a reservoir computing approach complying to echo-state network principles, along with varying strengths of time-delayed feedback to model a complex financial system. The derived model is demonstrated to act robustly towards influence of trends and other fluctuating parameters by effectively forecasting long-term system behavior. Moreover, it also re-generates the financial system unknowns with a high degree of accuracy when only limited future data is available, thereby, becoming a reliable feeder for any long-term decision making or policy formulations.

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Real-Time Emotion Recognition Using Echo State Networks

Cited by 7 publications

References 8 publications

Reservoir Computing With Truncated Normal Distribution for Speech Emotion Recognition

Reservoir Computing With Truncated Normal Distribution for Speech Emotion Recognition

A Review of Designs and Applications of Echo State Networks

A reservoir computing approach for forecasting and regenerating both dynamical and time-delay controlled financial system behavior

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