Punjabi Emotional Speech Database:Design, Recording and Verification

Kaur, Kamaldeep

doi:10.18201/ijisae.2021473641

Cited by 13 publications

(6 citation statements)

References 11 publications

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“…A Punjabi speech database was created by Kaur and Singh [32] consisting of 900 emotional speech utterances by 15 speakers. Happiness, fearful, angry, surprised, sad, and neutral are the six emotions present in the database.…”

Section: Emotional Speech Databases For Indo-aryan and Dravidianmentioning

confidence: 99%

A Review of the Advancement in Speech Emotion Recognition for Indo-Aryan and Dravidian Languages

Monisha

Sultana

2022

Advances in Human-Computer Interaction

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Speech emotion recognition (SER) has grown to be one of the most trending research topics in computational linguistics in the last two decades. Speech being the primary communication medium, understanding the emotional state of humans from speech and responding accordingly have made the speech emotion recognition system an essential part of the human-computer interaction (HCI) field. Although there are a few review works carried out for SER, none of them discusses the development of SER system for the Indo-Aryan or Dravidian language families. This paper focuses on some studies carried out for the development of an automatic SER system for Indo-Aryan and Dravidian languages. Besides, it presents a brief study of the prominent databases available for SER experiments. Some remarkable research works on the identification of emotion from the speech signal in the last two decades have also been discussed in this paper.

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Section: Emotional Speech Databases For Indo-aryan and Dravidianmentioning

confidence: 99%

A Review of the Advancement in Speech Emotion Recognition for Indo-Aryan and Dravidian Languages

Monisha

Sultana

2022

Advances in Human-Computer Interaction

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“…The database was designed, recorded and verified using various standards. 10 sentences in Punjabi language were recorded in each of the six basic emotions, specifically including happy, neutral, anger, fear, sad and surprise, with a total of 900 utterances [34].…”

Section: Punjabi Emotional Speech Corpus Preparationmentioning

confidence: 99%

Impact of Feature Extraction and Feature Selection Algorithms on Punjabi Speech Emotion Recognition Using Convolutional Neural Network

Kaur

Singh

2022

ACM Trans. Asian Low-Resour. Lang. Inf. Process.

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The challenge to refine the spontaneity and productivity of a machine and human coherence, speech emotion recognition has been an overriding area of research. The trustability and fulfillment of such emotion recognition are largely involved with the feature extraction and selection processes. An important role is played in exploring and distinguishing audio content during the feature extraction phase. Also, the features that have been extracted should be tough to a number of disturbances and reliable enough for an adequate classification system. This paper focuses on three main components of a Speech Emotion Recognition (SER) Process. The first one is the optimal feature extraction method for Punjabi SER system. The second one is the use of an appropriate feature selection method that desires to select effectual features from the ones extracted in the first step, and removes the redundant features, to improve the conduct of emotion recognition. The third one is the classification model that has been used further for emotion recognition. So, the scope of this paper is to explain the three main steps of Punjabi SER system, feature extraction, feature selection, and emotion recognition with classifier. The results have been calculated and compared for number of feature set combinations, with and without feature selection process. A total of 10 experiments are carried out and various performance metrics such as precision, recall, F1-score, accuracy, etc. are used to demonstrate the results.

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“…Deep learning (DL) forms more abstract high-level features by combining low-level features and nonlinear transformation, so that the model can better learn the distribution law of data [23] Xiaodan both put forward tree-like LSTM in 2015. The use of LSTM effectively solved the problem of disappearance and explosion in the process of RNN gradient return, and further optimized the depth model.…”

Section: Research Status Of Deep Neural Networkmentioning

confidence: 99%

Research On Text Classification Based On Deep Neural Network

Kim

2022

Int. j. commun. netw. inf. secur.

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Text classification is one of the classic tasks in the field of natural language processing. The goal is to identify the category to which the text belongs. Text categorization is widely used in email detection, sentiment analysis, topic marking and other fields. However, good text representation is the key to improve the performance of natural language processing tasks such as text classification. Traditional text representation adopts bag-of-words model or vector space model, which not only loses the context information of the text, but also faces the problems of high latitude and high sparsity. In recent years, with the increase of data and the improvement of computing performance, the use of deep learning technology to represent and classify texts has attracted great attention. Convolutional neural network, recurrent neural network and recurrent neural network with attention mechanism are used to represent the text, and then to classify the text and other natural language processing tasks, all of which have better performance than the traditional methods. In this paper, we design two sentence-level text representation and classification models based on the deep network. The details are as follows: (1) Text representation and classification model based on bidirectional cyclic and convolutional neural networks-BRCNN. Brcnn's input is the word vector corresponding to each word in the sentence; After using cyclic neural network to extract word order information in sentences, convolution neural network is used to extract higher-level features of sentences. After convolution, the maximum pool operation is used to obtain sentence vectors. At last, softmax classifier is used for classification. Cyclic neural network can capture the word order information in sentences, while convolutional neural network can extract useful features. Experiments on eight text classification tasks show that BRCNN model can get better text feature representation, and the classification accuracy rate is equal to or higher than that of the prior art.. (2) A text representation and classification model based on attention mechanism and convolutional neural network-ACNN. ACNN model uses the recurrent neural network with attention mechanism to obtain the context vector; Then convolution neural network is used to extract more advanced feature information. The maximum pool operation is adopted to obtain a sentence vector; At last, the softmax classifier is used to classify the text. Experiments on eight text classification benchmark data sets show that ACNN improves the stability of model convergence, and can converge to an optimal or local optimal solution better than BRCNN.

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Punjabi Emotional Speech Database:Design, Recording and Verification

Cited by 13 publications

References 11 publications

A Review of the Advancement in Speech Emotion Recognition for Indo-Aryan and Dravidian Languages

A Review of the Advancement in Speech Emotion Recognition for Indo-Aryan and Dravidian Languages

Impact of Feature Extraction and Feature Selection Algorithms on Punjabi Speech Emotion Recognition Using Convolutional Neural Network

Research On Text Classification Based On Deep Neural Network

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