Emotion Analysis from Speech of Different Age Groups

Palo, Hemanta Kumar; Mohanty, Mihir Narayan; Chandra, Mahesh

doi:10.15439/2017r21

Cited by 7 publications

(3 citation statements)

References 13 publications

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“…The best obtained accuracy was 90% when we have trained our model by using 128 Gaussian mixture models, and 5 number of HMMs states. Palo H K, and et al [8] have determined the age of speaker based on emotional speech prosody and clustering them using fuzzy c-means algorithm. This recognition of speech emotion based on suitable features provides age information that helped the society in different ways.…”

Section: Related Workmentioning

confidence: 99%

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The convolutional neural networks for Amazigh speech recognition system

Telmem

Ghanou

2021

TELKOMNIKA

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In this paper, we present an approach based on convolutional neural networks to build an automatic speech recognition system for the Amazigh language. This system is built with TensorFlow and uses mel frequency cepstral coefficient (MFCC) to extract features. In order to test the effect of the speaker's gender and age on the accuracy of the model, the system was trained and tested on several datasets. The first experiment the dataset consists of 9240 audio files. The second experiment the dataset consists of 9240 audio files distributed between females and males' speakers. The last experiment 3 the dataset consists of 13860 audio files distributed between age 9-15, age 16-30, and age 30+. The result shows that the model trained on a dataset of adult speaker's age +30 categories generates the best accuracy with 93.9%.

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Section: Related Workmentioning

confidence: 99%

“…And the classification part, the vector of feature extracted by the convolutive part is feed to the fully connected layers leading into the output layer which represents the classifier. The convolutive part consists of [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20].…”

Section: Cnnmentioning

confidence: 99%

The convolutional neural networks for Amazigh speech recognition system

Telmem

Ghanou

2021

TELKOMNIKA

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“…As the technology is enhancing and the use of electronic devices (such as mobile phone Google assistant, alexa) has been already introduced and in the peak demand as per the market value is concerned, the trade for the development of speech and audio analytic tools is kept on increasing. The research is going on not only in the linguistic areas [1] such as extracting message and working on words but also in paralinguistic areas such as Automatic identification of speaker [2], emotion analysis [3], [4] from speech. This area has a wide range of applications including telecom industry.…”

Section: Introductionmentioning

confidence: 99%

A rough set theory and deep learning based predictive system for gender recognition using audio speech

Yasmin

Das

Nayak

et al. 2021

Preprint

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Speech is one of the most delicate medium through which gender of the speakers can easily be identified. Though the related research has shown very good progress in machine learning but recently, deep learning has imparted a very good research area to explore the deficiency of gender discrimination using traditional machine learning techniques. In deep learning techniques, the speech features are automatically generated by the reinforcement learning from the raw data which have more discriminating power than the human generated features. But in some practical situations like gender recognition, it is observed that combination of both types of features sometimes provides comparatively better performance. In the proposed work, we have initially extracted and selected some informative and precise acoustic features relevant to gender recognition using entropy based information theory and Rough Set Theory (RST). Next, the audio speech signals are directly fed into the deep neural network model consists of Convolution Neural Network (CNN) and Gated Recurrent Unit network (GRUN) for extracting features useful for gender recognition. The RST selects precise and informative features, CNN extracts the locally encoded important features, and GRUN reduces the vanishing gradient and exploding gradient problems. Finally, a hybrid gender recognition system is developed combining both generated feature vectors. The developed model has been tested with five bench mark and a simulated dataset to evaluate its performance and it is observed that combined feature vector provides more effective gender recognition system specially when transgender is considered as a gender type together with male and female.

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Internet of Things (IoT) Application in Green Computing: An Overview

Das¹,

Palo

2020

Advances in Greener Energy Technologies

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Emotion Analysis from Speech of Different Age Groups

Cited by 7 publications

References 13 publications

The convolutional neural networks for Amazigh speech recognition system

The convolutional neural networks for Amazigh speech recognition system

A rough set theory and deep learning based predictive system for gender recognition using audio speech

Internet of Things (IoT) Application in Green Computing: An Overview

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