Emotion recognition on speech signals using machine learning

Ghai, Mohan; Lal, Shamit; Duggal, Shivam; Manik, Shrey

doi:10.1109/icbdaci.2017.8070805

Cited by 25 publications

(7 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In kNN, anger became 86.8%, fear became 93.7%, joy became 83.6%, neutral became 95.9%, and sadness became 96.3%. M. Ghai [8] selected the frame samples of the sound signals at 16000Hz and the selection duration 0.25 seconds of each frame for feature extraction. A. Iqbal [13] extracted 34 audio features from two datasets (RAVDESS and SAVEE) and selected frame size 0.05s and step size 0.025s.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Speech Emotion Recognition in Neurological Disorders Using Convolutional Neural Network

Zisad

Hossain

Andersson

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

Detecting emotions from the speech is one of the emergent research fields in the area of human information processing. Expressing emotion is a very di cult task for a person with neurological disorder. Hence, a Speech Emotion Recognition (SER) system may solve this by ensuring a barrier-less communication. Various research has been carried out in the area of SER. Therefore, the main objective of this research is to develop a system that can recognize emotion from the speech of a neurologically disordered person. Since convolutional neural network (CNN) is an e↵ective method, it has been considered to develop the system. The system uses tonal properties like MFCCs. RAVDESS audio speech and song databases for training and testing. In addition, a custom local dataset developed to support further training and testing. The performance of the proposed system compared with the traditional machine learning models as well as with the pre-trained CNN models including VGG16 and VGG19. The results demonstrate that the CNN model proposed in this research performed better than the mentioned machine learning techniques. This system enables one tohhhhhh classify eight emotions of neurologically disordered person including calm, angry, fearful, disgust, happy, surprise, neutral and sad.

show abstract

Section: Related Workmentioning

confidence: 99%

“…The weighted accuracy was 68.1% and unweighted accuracy was 67% of this model. Di↵erent frame sizes of 10-20 ms [6,8], etc., were selected in di↵erent works. Entropy, spectral entropy, MFCC, ZCR (zero-crossing rate), pitch, energy, etc., were the common features for audio data.…”

Section: Related Workmentioning

confidence: 99%

Speech Emotion Recognition in Neurological Disorders Using Convolutional Neural Network

Zisad

Hossain

Andersson

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

show abstract

“…To measure user experience, Alves et al [16] suggest that the main methods used are observation, think aloud, contextual, interviews/inquiries, prototyping, task analysis, cognitive walkthrough and questionnaires. Conventional methods for collecting and subsequently assessing user opinions are post-interaction, as a retrospective verbal or written self-report questionnaires [17][18][19]. However, despite allowing useful analysis, these methods depend on the users' interpretation and memory, as well as the accuracy and quality of the answers [5].…”

Section: User Experiencementioning

confidence: 99%

UXmood—A Sentiment Analysis and Information Visualization Tool to Support the Evaluation of Usability and User Experience

et al. 2019

View full text Add to dashboard Cite

This paper presents UXmood, a tool that provides quantitative and qualitative information to assist researchers and practitioners in the evaluation of user experience and usability. The tool uses and combines data from video, audio, interaction logs and eye trackers, presenting them in a configurable dashboard on the web. The UXmood works analogously to a media player, in which evaluators can review the entire user interaction process, fast-forwarding irrelevant sections and rewinding specific interactions to repeat them if necessary. Besides, sentiment analysis techniques are applied to video, audio and transcribed text content to obtain insights on the user experience of participants. The main motivations to develop UXmood are to support joint analysis of usability and user experience, to use sentiment analysis for supporting qualitative analysis, to synchronize different types of data in the same dashboard and to allow the analysis of user interactions from any device with a web browser. We conducted a user study to assess the data communication efficiency of the visualizations, which provided insights on how to improve the dashboard.

show abstract

“…Mohan G hai [12] in 2017 The main objective of this paper to speech emotion recognition and categorized into seven emotional states like anger, boredom, disgust, anxiety, happiness, sadness and neutral. The given method is based on the Mel Frequency Cepstral Coefficients (MFCC) used the Berlin database of emotional speech.…”

Section: Audio Analysis Andmentioning

confidence: 99%

Audio Analysis and Classification: A Review

Mohammad¹,

Tripathi²

2019

IJRAT

View full text Add to dashboard Cite

Communication plays a vital role according to the people's emotion, as emotions and gesture play 80% role while communication. Nowadays emotion recognition and classification are used in different areas to understand the human feelings like in the robotics, Health care, Military, Home automation, Hands-free computing, Mobile Telephony, Video game,call-center system, Marketing, etc. SER can help better interaction between the machine and the human. There are various algorithms and combination of the algorithms are available to recognize and classify the audio according to their emotion. In this paper, we attempted to investigate the episodic significant works, their technique and the impact of the approaches and the scope of the correction of the results.

show abstract

Emotion recognition on speech signals using machine learning

Cited by 25 publications

References 13 publications

Speech Emotion Recognition in Neurological Disorders Using Convolutional Neural Network

Speech Emotion Recognition in Neurological Disorders Using Convolutional Neural Network

UXmood—A Sentiment Analysis and Information Visualization Tool to Support the Evaluation of Usability and User Experience

Audio Analysis and Classification: A Review

Contact Info

Product

Resources

About