Emotion detection with hybrid voice quality and prosodic features using Neural Network

Idris, Inshirah; Salam, Sah Hj

doi:10.1109/wict.2014.7076906

Cited by 4 publications

(2 citation statements)

References 17 publications

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“…In [1][2][3] compares the speech emotion classification accuracy of speaker-based and the time to construct the model between Support Vector Machine(SVM) and Multi-Layer Perceptron(MLP) classifiers. The classification was performed with the WEKA unit, and the features were extracted with PRAAT.…”

Section: Literature Surveymentioning

confidence: 99%

A Study on Speech Emotion Recognitions on Machine Learning Algorithms

Amiripalli

Patnaik

et al. 2021

Recent Trends in Intensive Computing

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Speech emotion detection has been extremely relevant in today’s digital culture in recent years. RAVDESS, TESS, and SAVEE Datasets were used to train the model in our project. To determine the precision of each algorithm with each dataset, we looked at ten separate Machine Learning Algorithms. Following that, we cleaned the datasets by using the mask feature to eliminate unnecessary background noise, and then we applied all 10 algorithms to this clean speech dataset to improve accuracy. Then we look at the accuracies of all ten algorithms and see which one is the greatest. Finally, by using the algorithm, we could calculate the number of sound files correlated with each of the emotions described in those datasets.

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Section: Literature Surveymentioning

confidence: 99%

A Study on Speech Emotion Recognitions on Machine Learning Algorithms

Amiripalli

Patnaik

et al. 2021

Recent Trends in Intensive Computing

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show abstract

“…Researchers usually use two kinds of acoustic features for the speech emotion recognition. One is the global statistical features based on sentences, including prosodic features [3][4][5], power spectrum features [6,7] and voice quality features [8,9]. Seppänen et al [10] used 43-dimensional global prosodic features related to fundamental frequency, energy and duration to recognize the emotion of Finnish speech, and achieved 60% recognition rate in the case of speaker-independent.…”

Section: Introductionmentioning

confidence: 99%

Speech Emotion Recognition Using Multi-granularity Feature Fusion Through Auditory Cognitive Mechanism

et al. 2019

Cognitive Computing – ICCC 2019

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In this paper, we focus on the problems of single granularity in feature extraction, loss of temporal information and inefficient use of frame features in discrete speech emotion recognition. Firstly, preliminary cognitive mechanism of auditory emotion is explored through cognitive experiments, and then a multi-granularity fusion feature extraction method inspired by the mechanism for discrete emotional speech signals is proposed. The method can extract 3 different granularity features, including short-term dynamic features of frame granularity, dynamic features of segment granularity and long-term static features of global granularity. Finally, we use the LSTM network model to classify emotions according to the long-term and short-term characteristics of the fusion features. We implement experiment on the discrete emotion datasets of CHEAVD (CASIA Chinese Emotional AudioVisual Database) released by the Institute of automation, China Research Academy of Sciences, and achieved improvement in recognition rate, increasing the MAP by 6.48%.

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Literature Survey on Emotion Recognition for Social Signal Processing

Vijayalakshmi¹,

Mohanaiah

2019

Lecture Notes in Electrical Engineering

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Emotion detection with hybrid voice quality and prosodic features using Neural Network

Cited by 4 publications

References 17 publications

A Study on Speech Emotion Recognitions on Machine Learning Algorithms

A Study on Speech Emotion Recognitions on Machine Learning Algorithms

Speech Emotion Recognition Using Multi-granularity Feature Fusion Through Auditory Cognitive Mechanism

Literature Survey on Emotion Recognition for Social Signal Processing

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