A review on emotion recognition using speech

Basu, Supratim; Chakraborty, Jaybrata; Bag, Arnab; Aftabuddin, Md.

doi:10.1109/icicct.2017.7975169

Cited by 86 publications

(36 citation statements)

References 18 publications

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“…To achieve this goal, there are tools that perform the transcription of vocal interaction into text, together with the automatic annotation of prosodic elements [4]. Prosody can provide information about a speaker's mood, helping the recommender system to take decisions accordingly (for a survey about emotion recognition using speech see [3]). Future work will address the investigation of real possibilities and conditions to estimate confidence level and engagement from short vocal interactions (questions) and improve the personalization accordingly.…”

Section: Discussion and Related Workmentioning

confidence: 99%

Toward a User-Adapted Question/Answering Educational Approach

Alzetta

Adorni

Celik

et al. 2018

Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization

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This paper addresses the design of a model for Question/Answering in an interactive and mobile learning environment. The learner's question can be made through vocal interaction or typed text and the answer is the generation of a personalized learning path. This takes into account the focus and type of the question and some personal features of the learner extracted both from the question and prosodic features, in case of vocal questions. The response is a learning path that preserves the precedence of the prerequisite relations and contains all the relevant concepts for answering the user's question. The main contribution of the paper is to investigate the possibility to exploit educational concept maps in a Q/A interactive learning system.

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Section: Discussion and Related Workmentioning

confidence: 99%

Toward a User-Adapted Question/Answering Educational Approach

Alzetta

Adorni

Celik

et al. 2018

Adjunct Publication of the 26th Conference on User Modeling, Adaptation and Personalization

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“…These intrinsic challenges have led to the disagreement in the literature on which features are best for speech emotion recognition [20,36]. The trend in the literature shows that a combination of heterogeneous acoustic features is promising for speech emotion recognition [20,29,30,36,39], but how to effectively unify the different features is highly challenging [21,36]. The importance of selecting relevant features to improve the reliability of speech emotion recognition systems is strongly emphasized in the literature [3,20,29].…”

Section: Related Studiesmentioning

confidence: 99%

“…However, the set of features that one chooses to train the selected learning algorithm is one of the most important tools for developing effective speech emotion recognition systems [3,19]. Research has suggested that features extracted from the speech signal have a great effect on the reliability of speech emotion recognition systems [3,20], but selecting an optimal set of features is challenging [21].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

Zvarevashe

Olugbara

2020

Algorithms

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Automatic recognition of emotion is important for facilitating seamless interactivity between a human being and intelligent robot towards the full realization of a smart society. The methods of signal processing and machine learning are widely applied to recognize human emotions based on features extracted from facial images, video files or speech signals. However, these features were not able to recognize the fear emotion with the same level of precision as other emotions. The authors propose the agglutination of prosodic and spectral features from a group of carefully selected features to realize hybrid acoustic features for improving the task of emotion recognition. Experiments were performed to test the effectiveness of the proposed features extracted from speech files of two public databases and used to train five popular ensemble learning algorithms. Results show that random decision forest ensemble learning of the proposed hybrid acoustic features is highly effective for speech emotion recognition.

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“…Natural speech is greatly affected by external factors which may lead to a reduction of recognition accuracy. Therefore, before the SER system can be established, it is necessary to collect an emotional corpus according to emotion description methods and record a high-quality emotion speech database [33].…”

Section: Introductionmentioning

confidence: 99%

Recognize basic emotional statesin speech by machine learning techniques using mel-frequency cepstral coefficient features

Yang

Dey

Sherratt

et al. 2020

IFS

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AI based Speech Recognition has been widely used in the consumer field for control of smart home personal assistants, with many such devices on the market. Smart home assistants that could detect the user's emotion would improve the communication between a user and the assistant enabling the assistant to offer more productive feedback. Thus, the aim of this work is to analyze emotional states in speech and propose a suitable algorithm considering performance verses complexity for deployment in smart home devices. The four emotional speeches were selected from the Berlin Emotional Database (EMO-DB) as experimental data, 26 MFCC features were extracted from each type of emotional speech to identify the emotions of happiness, anger, sadness and neutrality. Then, speaker-independent experiments for our Speech emotion Recognition (SER) were conducted by using the Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM), Probabilistic Neural Network (PNN) and Support Vector Machine (SVM). Synthesizing the recognition accuracy and processing time, this work shows that the performance of SVM was the best among the four methods as a good candidate to be deployed for SER in smart home devices. SVM achieved an overall accuracy of 92.4% while offering low computational requirements when training and testing. We conclude that the MFCC features and the SVM classification models used in speaker-independent experiments are highly effective in the automatic prediction of emotion.

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A review on emotion recognition using speech

Cited by 86 publications

References 18 publications

Toward a User-Adapted Question/Answering Educational Approach

Toward a User-Adapted Question/Answering Educational Approach

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

Recognize basic emotional statesin speech by machine learning techniques using mel-frequency cepstral coefficient features

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