Structural Support Vector Machine for Speech Recognition Classification with CNN Approach

Chouhan, Kuldeep; Singh, Abhishek; Shrivastava, Anurag; Agrawal, Shweta; Shukla, Brahma Datta; Tomar, Pragya Singh

doi:10.1109/citsm52892.2021.9588918

Cited by 28 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…By removing noise and unstable components from the data, the local features of the network traffic can be effectively captured. Although CNNs have achieved great success in the field of image processing (i.e., considering two-dimensional data), one-dimensional (1D) data are suitable for processing time-series, such as time series for speech recognition [ 24 ], stock prediction [ 25 ], etc.…”

Section: Predictive Modelsmentioning

confidence: 99%

Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network

Pan

Wang

Shi

et al. 2022

Sensors

View full text Add to dashboard Cite

Network traffic prediction is an important tool for the management and control of IoT, and timely and accurate traffic prediction models play a crucial role in improving the IoT service quality. The degree of burstiness in intelligent network traffic is high, which creates problems for prediction. To address the problem faced by traditional statistical models, which cannot effectively extract traffic features when dealing with inadequate sample data, in addition to the poor interpretability of deep models, this paper proposes a prediction model (fusion prior knowledge network) that incorporates prior knowledge into the neural network training process. The model takes the self-similarity of network traffic as a priori knowledge, incorporates it into the gating mechanism of the long short-term memory neural network, and combines a one-dimensional convolutional neural network with an attention mechanism to extract the temporal features of the traffic sequence. The experiments show that the model can better recover the characteristics of the original data. Compared with the traditional prediction model, the proposed model can better describe the trend of network traffic. In addition, the model produces an interpretable prediction result with an absolute correction factor of 76.4%, which is at least 10% better than the traditional statistical model.

show abstract

Section: Predictive Modelsmentioning

confidence: 99%

Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network

Pan

Wang

Shi

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…A posted tweet can be visible by default to the users unless they revolute their confidentialities to make it understandable by the followers on Twitter incline [7]. The eminence of tweet sentiment polarity classifications has information and emerging analytics applications [8]. In this research work, a novel approach is proposed to represent expressions in the semantic direction, based on data abstraction, preprocessing of extracted data and classification on Twitter.…”

Section: Introductionmentioning

confidence: 99%

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Chouhan¹,

Yadav²,

Rout³

et al. 2023

Computer Systems Science and Engineering

View full text Add to dashboard Cite

Twitter is a radiant platform with a quick and effective technique to analyze users' perceptions of activities on social media. Many researchers and industry experts show their attention to Twitter sentiment analysis to recognize the stakeholder group. The sentiment analysis needs an advanced level of approaches including adoption to encompass data sentiment analysis and various machine learning tools. An assessment of sentiment analysis in multiple fields that affect their elevations among the people in real-time by using Naive Bayes and Support Vector Machine (SVM). This paper focused on analysing the distinguished sentiment techniques in tweets behaviour datasets for various spheres such as healthcare, behaviour estimation, etc. In addition, the results in this work explore and validate the statistical machine learning classifiers that provide the accuracy percentages attained in terms of positive, negative and neutral tweets. In this work, we obligated Twitter Application Programming Interface (API) account and programmed in python for sentiment analysis approach for the computational measure of user's perceptions that extract a massive number of tweets and provide market value to the Twitter account proprietor. To distinguish the results in terms of the performance evaluation, an error analysis investigates the features of various stakeholders comprising social media analytics researchers, Natural Language Processing (NLP) developers, engineering managers and experts involved to have a decision-making approach.

show abstract

“…Asghar et al ( 2022 ) proposed that using amplitude and frequency spectral features (MSFs) and mel-frequency cepstral coefficients (MFCCs), perceptual weighted linear predictive (PLP) and perceptual features had achieved good speech emotion recognition effects. Chouhan et al ( 2021 ) used CNN and SVM to classify and recognize speech emotion and achieved good results. Kaur and Kumar ( 2021 ) adopted CNN for speech emotion recognition in the data set, which greatly improved the ability of speech emotion recognition.…”

Section: Introductionmentioning

confidence: 99%

Global and local feature fusion via long and short-term memory mechanism for dance emotion recognition in robot

Lyu

Sun

2022

Front. Neurorobot.

View full text Add to dashboard Cite

In recent years, there are more and more intelligent machines in people's life, such as intelligent wristbands, sweeping robots, intelligent learning machines and so on, which can simply complete a single execution task. We want robots to be as emotional as humans. In this way, human-computer interaction can be more natural, smooth and intelligent. Therefore, emotion research has become a hot topic that researchers pay close attention to. In this paper, we propose a new dance emotion recognition based on global and local feature fusion method. If the single feature of audio is extracted, the global information of dance cannot be reflected. And the dimension of data features is very high. In this paper, an improved long and short-term memory (LSTM) method is used to extract global dance information. Linear prediction coefficient is used to extract local information. Considering the complementarity of different features, a global and local feature fusion method based on discriminant multi-canonical correlation analysis is proposed in this paper. Experimental results on public data sets show that the proposed method can effectively identify dance emotion compared with other state-of-the-art emotion recognition methods.

show abstract

Structural Support Vector Machine for Speech Recognition Classification with CNN Approach

Cited by 28 publications

References 16 publications

Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network

Network Traffic Prediction Incorporating Prior Knowledge for an Intelligent Network

Sentiment Analysis with Tweets Behaviour in Twitter Streaming API

Global and local feature fusion via long and short-term memory mechanism for dance emotion recognition in robot

Contact Info

Product

Resources

About