Multilingual vocal emotion recognition and classification using back propagation neural network

Kayal, Apoorva J.; Nirmal, Jagannath

doi:10.1063/1.4942736

Cited by 5 publications

(3 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As the project advances, potential enhancements include integrating SER with LMS platforms for synchronized data exchange [5], implementing AI algorithms for personalized learning, developing mobile applications for broader accessibility, exploring blockchain for enhanced data security, and adapting SER for global use with multilingual support. Additionally, adapting SER to accommodate multiple languages will broaden its global utility and accessibility.…”

Section: A Future Directionsmentioning

confidence: 99%

A Survey on Speech Emotion Based Music Recommendation System

Kothawade

2024

IJSREM

View full text Add to dashboard Cite

Psychological research has proven that music relieves stress, elevates mood, and is responsible for the release of “feel-good” chemicals like oxytocin, serotonin, and dopamine. It comes as no surprise that music has been a popular tool in rehabilitation centers and therapy for various disorders, thus with the interminably rising numbers of people facing mental health-related issues across the globe, addressing mental health concerns is more crucial than ever. Despite the existing music recommendation systems, there is a dearth of holistically curated algorithms that take care of the needs of users. Given that, an undeniable majority of people turn to music on a regular basis and that music has been proven to increase cognition, memory, and sleep quality while reducing anxiety, pain, and blood pressure, it is the need of the hour to fashion a product that extracts all the benefits of music in the most extensive and deployable method possible. Our project aims to ameliorate our users’ mental state by building a comprehensive mood-based music recommendation system called “Viby”. Keywords Language, Communication, Speech recognition, Interaction.

show abstract

Section: A Future Directionsmentioning

confidence: 99%

A Survey on Speech Emotion Based Music Recommendation System

Kothawade

2024

IJSREM

View full text Add to dashboard Cite

show abstract

“…Sequential minimal optimization (SMO), J48, and random forest have been used for testing the adaptive data boosting (ADB) technique [21]. For ANN, it can be seen that the models used are ANN with 3 layers [22], DNN [23], progressive neural network [24], recurrent neural network [12,25], backpropagation neural network [26], deep convolutional recurrent network [27], coupled deep convolutional neural network (CDCNN) [28], deep belief networks (DBNs) [29], combination of SVM and belief networks [30], CNN [31], convolutional recurrent neural network (CRNN) [32], deep learning [33,34], a combination of convolutional and recurrent layers for reusing ASR (automatic speech recognition) network [35] and LSTM (long short-term memory) network [36]. A number of issues have also been raised for emotion recognition of speech, such as transfer learning [37][38][39], using cross-corpus [27,40], and adversarial training [41].…”

Section: Related Workmentioning

confidence: 99%

Emotion Recognition with Capsule Neural Network

Van¹,

Nguyen²,

Le³

2022

Computer Systems Science and Engineering

View full text Add to dashboard Cite

“…DNN speech network models have been successfully applied to a variety of speech classification tasks, demonstrating that high accuracy in speech attribute detection and phoneme estimation can be achieved using DNNs [9]. [10] classify different emotions from different languages by constructing an artificial neural network (ANN). The generation of feature sets is performed using the Mel Frequency Cepstrum Coefficient (MFCC) and Short Term Energy (STE).…”

Section: Introductionmentioning

confidence: 99%

Combined spectral and speech features for pig speech recognition

Zhou

Chen

et al. 2022

PLoS ONE

View full text Add to dashboard Cite

The sound of the pig is one of its important signs, which can reflect various states such as hunger, pain or emotional state, and directly indicates the growth and health status of the pig. Existing speech recognition methods usually start with spectral features. The use of spectrograms to achieve classification of different speech sounds, while working well, may not be the best approach for solving such tasks with single-dimensional feature input. Based on the above assumptions, in order to more accurately grasp the situation of pigs and take timely measures to ensure the health status of pigs, this paper proposes a pig sound classification method based on the dual role of signal spectrum and speech. Spectrograms can visualize information about the characteristics of the sound under different time periods. The audio data are introduced, and the spectrogram features of the model input as well as the audio time-domain features are complemented with each other and passed into a pre-designed parallel network structure. The network model with the best results and the classifier were selected for combination. An accuracy of 93.39% was achieved on the pig speech classification task, while the AUC also reached 0.99163, demonstrating the superiority of the method. This study contributes to the direction of computer vision and acoustics by recognizing the sound of pigs. In addition, a total of 4,000 pig sound datasets in four categories are established in this paper to provide a research basis for later research scholars.

show abstract

Multilingual vocal emotion recognition and classification using back propagation neural network

Cited by 5 publications

References 11 publications

A Survey on Speech Emotion Based Music Recommendation System

A Survey on Speech Emotion Based Music Recommendation System

Emotion Recognition with Capsule Neural Network

Combined spectral and speech features for pig speech recognition

Contact Info

Product

Resources

About