Hybrid Approach for Emotion Classification of Audio Conversation Based on Text and Speech Mining

Bhaskar, Jasmine; Sruthi, K; Nedungadi, Prema

doi:10.1016/j.procs.2015.02.112

Cited by 80 publications

(33 citation statements)

References 6 publications

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“…Luengo et al [20] used 324 spectral and 54 prosody features combined with five voice quality features to test their proposed speech emotion recognition method on the Surrey audio-visual expressed emotion (SAVEE) database after applying the minimal redundancy maximal relevance (mRMR) to reduce less discriminating features. In [28], a method for recognizing emotions in an audio conversation based on speech and text was proposed and tested on the SemEval-2007 database using SVM. Liu et al [29] used the extreme learning machine (ELM) method for feature selection that was applied to 938 features based on a combination of spectral and prosodic features from Emo-DB for speech emotion recognition.…”

Section: Related Studiesmentioning

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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Section: Related Studiesmentioning

confidence: 99%

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

Zvarevashe

Olugbara

2020

Algorithms

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“…Previously various researchers have worked towards improving the performance of depression recommendation. One of these works is mentioned in [1], where authors have used speech, textual data with support vector machines (SVM) to perform sentiment analysis. They use WordNet Affect and SentiWordNet for language processing and pitch, energy, formants, intensity & zero crossing rate (ZCR) features for sound processing to claim 81% accuracy, which can be further improved using deep learning algorithms like deep-nets and Q-learning.…”

Section: Literature Reviewmentioning

confidence: 99%

Improving Accuracy of Sentiment Analysis for Depression Recommendation using Multi-Domain Fuzzy Rules

Mamtora*,

Ragha

2020

IJITEE

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Social media & e-commerce has opened up the doors for human behavioral analysis in ways which were not possible before. Companies have the ability to track user’s mood and suggest advertisements which can trigger buying decisions based on it. This is possible due to user’s real time social media updates. Users nowadays are willing to provide information like their location, their age, nearby friends information, their mood, their buying patterns, etc. Companies do not intentionally collect all this information, but it has become a matter of social pride to post it as social media status and updates. The information available can be put to use in multiple forms- predict election results, movie success, product liking/disliking, travel destination recommendation, health care, etc. In our work, we utilize this textual information posted by different users and analyze their depression level focusing on negative sentiments. In order to perform this task, we have considered user’s tweets, any links which they might have posted, the time of the tweet, their age group and any previous depression history of the user. All these parameters are given to a novel fuzzy decision tree that uses sentiment analysis and game theory-based scoring in order to evaluate the depression score for the user. We analyzed the system on different real-time users, and observed that the system predicts depression level with more than 90% accuracy. Our work can be used to generate a prototype to identify if a person is in a depressive state and figure out the intensity of his/her depression.

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“…When using RBF the choice of two parameters is very important. Penalty C in case of conflict and constant σ in (2) and (3). If the values of C and σ are identified, then the classifier can predict emotions more accurately.…”

Section: Emotion Classificationmentioning

confidence: 99%

“…Recently, many research projects have been conducted in order to use this kind of data to achieve better results. Several hybrid approaches have been proposed for emotion classification based on text, speech, and image [2]. Changes in the voice are independent of the speaker and the language.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Proposing A Hybrid Approach for Emotion Classification using Audio and Video Data

Rafeh¹,

Khojasteh²,

Alobaidi³

2019

5th International Conference on Computer Science and Information Technology (CSTY 2019)

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Emotion recognition has been a research topic in the field of Human Computer Interaction (HCI) during recent years. Computers have become an inseparable part of human life. Users need human-like interaction to better communicate with computers. Many researchers have become interested in emotion recognition and classification using different sources. A hybrid approach of audio and text has been recently introduced. All such approaches have been done to raise the accuracy and appropriateness of emotion classification. In this study, a hybrid approach of audio and video has been applied for emotion recognition. The innovation of this approach is selecting the characteristics of audio and video and their features as a unique specification for classification. In this research, the SVM method has been used for classifying the data in the SAVEE database. The experimental results show the maximum classification accuracy for audio data is 91.63% while by applying the hybrid approach the accuracy achieved is 99.26%.

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Hybrid Approach for Emotion Classification of Audio Conversation Based on Text and Speech Mining

Cited by 80 publications

References 6 publications

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition

Improving Accuracy of Sentiment Analysis for Depression Recommendation using Multi-Domain Fuzzy Rules

Proposing A Hybrid Approach for Emotion Classification using Audio and Video Data

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