Deep Learning Techniques for Speech Emotion Recognition: A Review

Pandey, Sandeep; Shekhawat, Hanumant Singh; Prasanna, S. R. Mahadeva

doi:10.1109/radioelek.2019.8733432

Cited by 66 publications

(34 citation statements)

References 17 publications

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“…The SER plays an important role in the HCI, and researchers are making a variety of techniques in the current decade to make it efficient and robust for real-time applications [ 4 ]. In the past decade, it has been a challenging task to recognize the emotional facts and the expressive cues from the speech of an individual due to the lack of techniques and technologies [ 5 , 6 ]. In each era, researchers have worked to develop an efficient SER system, and they have developed several methods for preprocessing, features extraction, and classification [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Deep-Net: A Lightweight CNN-Based Speech Emotion Recognition System Using Deep Frequency Features

Tursunov

Mustaqeem

Kwon

2020

Sensors

121

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Artificial intelligence (AI) and machine learning (ML) are employed to make systems smarter. Today, the speech emotion recognition (SER) system evaluates the emotional state of the speaker by investigating his/her speech signal. Emotion recognition is a challenging task for a machine. In addition, making it smarter so that the emotions are efficiently recognized by AI is equally challenging. The speech signal is quite hard to examine using signal processing methods because it consists of different frequencies and features that vary according to emotions, such as anger, fear, sadness, happiness, boredom, disgust, and surprise. Even though different algorithms are being developed for the SER, the success rates are very low according to the languages, the emotions, and the databases. In this paper, we propose a new lightweight effective SER model that has a low computational complexity and a high recognition accuracy. The suggested method uses the convolutional neural network (CNN) approach to learn the deep frequency features by using a plain rectangular filter with a modified pooling strategy that have more discriminative power for the SER. The proposed CNN model was trained on the extracted frequency features from the speech data and was then tested to predict the emotions. The proposed SER model was evaluated over two benchmarks, which included the interactive emotional dyadic motion capture (IEMOCAP) and the berlin emotional speech database (EMO-DB) speech datasets, and it obtained 77.01% and 92.02% recognition results. The experimental results demonstrated that the proposed CNN-based SER system can achieve a better recognition performance than the state-of-the-art SER systems.

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Section: Introductionmentioning

confidence: 99%

Deep-Net: A Lightweight CNN-Based Speech Emotion Recognition System Using Deep Frequency Features

Tursunov

Mustaqeem

Kwon

2020

Sensors

121

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“…For each phase, the data were mapped onto a mel-spectrogram space [36]. To obtain the MFCC, the Fourier discrete cosine transform of the logarithm of each frame of the mel-spectrogram was computed (figure 2), thus dramatically reducing the dimensionality of the features.…”

Section: (C) Mel-spectrogram and Mel-frequency Cepstrum Coefficientsmentioning

confidence: 99%

A novel multi-branch architecture for state of the art robust detection of pathological phonocardiograms

Duggento

Conti

Guerrisi

et al. 2021

Phil. Trans. R. Soc. A.

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Heart auscultation is an inexpensive and fundamental technique to effectively diagnose cardiovascular disease. However, due to relatively high human error rates even when auscultation is performed by an experienced physician, and due to the not universal availability of qualified personnel, e.g. in developing countries, many efforts are made worldwide to propose computational tools for detecting abnormalities in heart sounds. The large heterogeneity of achievable data quality and devices, the variety of possible heart pathologies, and a generally poor signal-to-noise ratio make this problem very challenging. We present an accurate classification strategy for diagnosing heart sounds based on (1) automatic heart phase segmentation, (2) state-of-the art filters drawn from the field of speech synthesis (mel-frequency cepstral representation) and (3) an ad hoc multi-branch, multi-instance artificial neural network based on convolutional layers and fully connected neuronal ensembles which separately learns from each heart phase hence implicitly leveraging their different physiological significance. We demonstrate that it is possible to train our architecture to reach very high performances, e.g. an area under the curve of 0.87 or a sensitivity of 0.97. Our machine-learning-based tool could be employed for heartsound classification, especially as a screening tool in a variety of situations including telemedicine applications. This article is part of the theme issue ‘Advanced computation in cardiovascular physiology: new challenges and opportunities’.

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“…Detecting group emotion is more complicated than detecting an individual emotion, as estimating the mood of a group is not necessarily similar to simply combining all individual emotions in that group, a topic on which extensive work has been done (see [12], [13], and [14] for some recent reviews on individual-based, rather than group-based, emotion recognition). In [9], a user survey was developed to investigate which factors influence group emotion perception.…”

Section: Introductionmentioning

confidence: 99%

Automatic Emotion Recognition for Groups: A Review

Veltmeijer

Gerritsen

Hindriks

2023

IEEE Trans. Affective Comput.

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This review aims to summarize and describe research on the topic of automatic group emotion recognition. In recent years, the topic of emotion analysis of groups or crowds has gained interest, with studies performing emotion detection in different contexts, using different datasets and modalities (such as images, video, audio, social media messages), and taking different approaches. Articles are included after an innovative search method, including Dense Query Extraction and automatic cross-referencing. Discussed are the types of groups and emotion models considered in automatic emotion recognition research, common datasets for all modalities, general approaches taken, and reported performances. These performances are discussed, followed by an analysis of the application possibilities of the discussed methods. To ensure clear, replicable, and comparable studies, we suggest research should test on multiple, common datasets and report on multiple metrics, when possible. Implementation details and code should be made available where possible. An area of interest for future work is to build systems with more real-world application possibilities, coping with changing group sizes, different emotional subgroups, and changing emotions over time, while having a higher robustness and working with datasets with reduced biases.

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Deep Learning Techniques for Speech Emotion Recognition: A Review

Cited by 66 publications

References 17 publications

Deep-Net: A Lightweight CNN-Based Speech Emotion Recognition System Using Deep Frequency Features

Deep-Net: A Lightweight CNN-Based Speech Emotion Recognition System Using Deep Frequency Features

A novel multi-branch architecture for state of the art robust detection of pathological phonocardiograms

Automatic Emotion Recognition for Groups: A Review

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