ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Software Effort Estimation Framework to Improve Organization Productivity Using Emotion Recognition of Software Engineers in Spontaneous Speech


Affiliations
1 School of Computing Science and Engineering, VIT University, Chennai Campus, India
2 Department of Computer Science and Systems Engineering, Andhra University, India
     

   Subscribe/Renew Journal


Productivity is a very important part of any organisation in general and software industry in particular. Now a day's Software Effort estimation is a challenging task. Both Effort and Productivity are inter-related to each other. This can be achieved from the employee's of the organization. Every organisation requires emotionally stable employees in their firm for seamless and progressive working. Of course, in other industries this may be achieved without man power. But, software project development is labour intensive activity. Each line of code should be delivered from software engineer. Tools and techniques may helpful and act as aid or supplementary. Whatever be the reason software industry has been suffering with success rate. Software industry is facing lot of problems in delivering the project on time and within the estimated budget limit. If we want to estimate the required effort of the project it is significant to know the emotional state of the team member. The responsibility of ensuring emotional contentment falls on the human resource department and the department can deploy a series of systems to carry out its survey. This analysis can be done using a variety of tools, one such, is through study of emotion recognition. The data needed for this is readily available and collectable and can be an excellent source for the feedback systems. The challenge of recognition of emotion in speech is convoluted primarily due to the noisy recording condition, the variations in sentiment in sample space and exhibition of multiple emotions in a single sentence. The ambiguity in the labels of training set also increases the complexity of problem addressed. The existing models using probabilistic models have dominated the study but present a flaw in scalability due to statistical inefficiency. The problem of sentiment prediction in spontaneous speech can thus be addressed using a hybrid system comprising of a Convolution Neural Network and Hidden Markov Model.

Keywords

Estimation, Productivity, Deep Neural Networks, CNN, Sentiment Prediction, Spontaneous Speech, HMM.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Yelin Kim, Honglak Lee and Emily Mower Provost, “Deep learning for robust feature generation in audiovisual emotion recognition in Acoustics, Speech and Signal Processing”, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3687-3691, 2013.

  • Yoshua Bengio, “Learning deep architectures for AI”, Foundations and trend in Machine Learning, Vol. 2, No.1, pp. 1-127, 2009.

  • Mohamed R. Amer, Behjat Siddiquie, Colleen Richey and Ajay Divakaran , “A. Emotion Detection in Speech using Deep Neural Networks”, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 3724-3728, 2014.

  • Abdel-rahman Mohamed, George E. Dahl and Geoffrey Hinton, “Acoustic modeling using deep belief networks”, IEEE Transactions on Audio, Speech, and Language Processing, Vol. 20, No. 1, pp. 14-22, 2012.

  • Geovany A Ramirez, Tadas Baltrusaitis and Louis-Philippe Morency, “Modeling latent discriminative dynamic of multi-dimensional affective”, Affective Computing and Intelligent Interaction, pp. 396-406, 2011.

  • B. Siddiquie, S. Khan, A. Divakaran and H. Sawhney, “Affect analysis in natural human interaction using Joint Hidden Conditional Random Fields”, Proceedings of IEEE International Conference on Multimedia and Expo, pp. 1-6, 2013.

  • Bjorn Schuller, Michel Valstar, Florian Eyben, Gary McKeown, Roddy Cowie and Maja Pantic, “Avec 2011–the first international audio/visual emotion challenge”, Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction, Vol. 6975, pp. 415-424, 2011.

  • Michael Glodek, Stephan Tschechne, Georg Layher, Martin Schels, Tobias Brosch, Stefan Scherer, Markus Kachele, Miriam Schmidt, Heiko Neumann, Günther Palm and Friedhelm SchwenkerShow less, “Multiple classifier systems for the classification of audio-visual emotional states”, Proceedings of the 4th International Conference on Affective Computing and Intelligent Interaction, Vol. 6975, pp. 359-368, 2011.

  • Geoffrey E. Hinton, Simon Osindero and Yee-Whye Teh, “A fast learning algorithm for deep belief nets”, Neural Computation, Vol. 18, No. 7, pp. 1527-1554, 2006.

  • Kevin Patrick Murphy, “Dynamic bayesian networks: representation, inference and learning”, University of California, Berkeley, pp. 1-212, 2002.

  • Alex Krizhevsky, Ilya Sutskever and Geoffrey E. Hinton, “Imagenet classification with deep convolutional neural networks”, Advances in Neural Information Processing Systems, pp. 1097-1105, 2012.

  • Ronan Collobert and Jason Weston, “A unified architecture for natural language processing: Deep neural networks with multitask learning”, Proceedings of the 25th International Conference on Machine Learning, pp. 160-167, 2008.

  • Xavier Glorot, Antoine Bordes and Yoshua Bengio, “Domain adaptation for large-scale sentiment classification: A deep learning approach”, Proceedings of the 28th International Conference on Machine Learning, pp. 513-520, 2011.

  • Jiquan Ngiam, Aditya Khosla, Mingyu Kim, Juhan Nam, Honglak Lee and Andrew Y. Ng, “Multimodal deep learning”, Proceedings of the 28th International Conference on Machine Learning, pp. 689-696, 2011.

  • Ian J. Goodfellow, David Warde-Farley, Mehdi Mirza, Aaron Courville and Yoshua Bengio, “Maxout networks”, Proceedings of 30th International Conference on Machine Learning, Vol. 28, 2013.

  • Honglak Lee, Peter Pham, Yan Largman and Andrew Y. Ng, “Unsupervised feature learning for audio classification using convolutional deep belief networks”, Advances in Neural Information Processing Systems, pp. 1096-1104, 2009.

  • James Bergstra, Olivier Breuleux, Frédéric Bastien, Pascal Lamblin, Razvan Pascanu, Guillaume Desjardins, Joseph Turian, David Warde-Farley and Yoshua Bengio, “Theano: a CPU and GPU math expression compiler”, Proceedings of the Python for Scientific Computing Conference, 2010.

  • Yann LeCun and Yoshua Bengio, “Convolutional networks for images, speech, and time series”, The Handbook of Brain Theory and Neural Networks”, pp. 255-258, 1995.

  • D. S. Yeung, Y. T. Cheng and H. S. Fong, “Neocognitron based handwriting recognition system performance tuning using genetic algorithm”, Proceedings of IEEE International Conference in Systems, Man, and Cybernetics, Vol. 5, pp. 4228-4233, 1998.

  • Bjorn Schuller, Gerhard Rigoll, and Manfred Lang, “Hidden Markov model-based speech emotion recognition”, Proceedings of IEEE International Conference on Acoustics, Speech, & Signal Processing, Vol. 2, pp. 401-404, 2003.

  • Moataz El Ayadi, Mohamed S. Kamel and Fakhri Karray, “Survey on speech emotion recognition: Features, classification schemes, and databases”, Pattern Recognition, Vol. 44, No. 3, pp. 572-587, 2011.

  • Yi – Lin Lin, and Gang Wei, “Speech emotion recognition based on HMM and SVM”, Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vol. 8, pp. 4898-4901, 2005.

  • Lawrence R. Rabiner, “A Tutorial on Hidden Markov Models and selected applications in Speech Recognition”, Proceedings of the IEEE, Vol. 77, No. 2, 257-286, 1989.

  • F. Burkhardt, A. Paeschke, M. Rolfes, W. F. Sendlmeier and B. Weiss, “A database of German emotional speech”, Proceeding Interspeech, Vol. 5, pp. 1517-1520, 2005.

  • G. R. Finnie, G. E. Wittig and J. M. Desharnais, “A Comparison of Software Effort Estimation Techniques: Using Function Points with Neural Networks, Case-Based Reasoning and Regression Models”, Journal of Systems and Software, Vol. 39, No. 3, pp. 281-289, 1997.

  • Gopalaswamy Ramesh and Ramesh Bhattiprolu, “Software Maintenance - Effective Practices for Geographically Distributed Environments”, Tata Mcgraw Hill, 2009.

  • Krishnamoorthy Srinivasan and Douglas Fisher, “Machine Learning Approaches to Estimating Software Development Effort”, IEEE Transactions on Software Engineering, Vol. 21, No. 2, pp. 126-137, 1995.

  • Steve. McConnell, “Code Complete: A Practical Handbook of Software Construction”, Second Edition, Microsoft Press, 2004.

  • M. A. Parthasarathy, “Practical Software Estimation – Function Point Methods for Insourced and Outsourced Projects”, Addison-Wesley & Infosys Press, 2012.

  • B.V.A.N.S.S. Prabhakar Rao and P. Sita Ramaiah “Adaptive System for Estimating Development Effort”, Journal of CNS, Vol. 1, No. 1, pp 52-56, 2012.

  • B.V.A.N.S.S. Prabhakar Rao and P. Seetha Ramaiah; “Software Size Estimation Using Fuzzy Backpropagation Network Method”, International Journal of Computer Science, Vol. 9, No. 1, pp. 339-348, 2012.

  • B.V.A.N.S.S. Prabhakar Rao and P. Seetha Ramaiah; “A novel approach to design Neuro-fuzzy expert system for software estimation”, International Journal of Engineering Research & Technology, Vol. 2, No. 13, pp. 3012-3017, 2013.

  • Ali Bou Nassif and Luiz Fernando Capretz, “Towards an early software estimation using log-linear regression and multilayer perceptron model”, The Journal of Systems and Software, Vol. 86, No. 1, pp. 144-160, 2013.

  • Ekrem Kocaguneli and Tim Menzies, “Software effort models should be assessed via leave-one-out validation”, Journal of Systems and Software, Vol. 86, No. 7, pp. 1879-1890, 2013.

  • Moataz A. Ahmed, Irfan Ahmad, and Jarallah S. AlGhamdi, “Probabilistic size proxy for software effort prediction: A framework”, Journal of Information and Software Technology, Vol. 55, No. 2, pp. 241-251, 2013.

  • Leandro L. Minku and Xin Yao, “Ensembles and locality: Insight on improving software effort estimation”, Information and Software Technology, Vol. 55, No. 8, pp. 1512-1528, 2013.

  • Yeong-Seok Seo, Doo-Hwan Bae, and Ross Jeffery, “AREION: Software effort estimation based on multiple regressions with adaptive recursive data partitioning”, Information and Software Technology, Vol. 55, No. 10, pp. 1710-1725, 2013.

  • Ye Yang, Zhimin He, Ke Mao, Qi Li, Vu Nguyen, Barry Boehm, and Ricardo Valerdi, “Analyzing and handling local bias for calibrating parametric cost estimation models”, Information and Software Technology, Vol. 55, No. 8, pp. 1496-1511, 2013.


Abstract Views: 306

PDF Views: 4




  • Software Effort Estimation Framework to Improve Organization Productivity Using Emotion Recognition of Software Engineers in Spontaneous Speech

Abstract Views: 306  |  PDF Views: 4

Authors

B. V. A. N. S. S. Prabhakar Rao
School of Computing Science and Engineering, VIT University, Chennai Campus, India
P. Seetha Ramaiah
Department of Computer Science and Systems Engineering, Andhra University, India

Abstract


Productivity is a very important part of any organisation in general and software industry in particular. Now a day's Software Effort estimation is a challenging task. Both Effort and Productivity are inter-related to each other. This can be achieved from the employee's of the organization. Every organisation requires emotionally stable employees in their firm for seamless and progressive working. Of course, in other industries this may be achieved without man power. But, software project development is labour intensive activity. Each line of code should be delivered from software engineer. Tools and techniques may helpful and act as aid or supplementary. Whatever be the reason software industry has been suffering with success rate. Software industry is facing lot of problems in delivering the project on time and within the estimated budget limit. If we want to estimate the required effort of the project it is significant to know the emotional state of the team member. The responsibility of ensuring emotional contentment falls on the human resource department and the department can deploy a series of systems to carry out its survey. This analysis can be done using a variety of tools, one such, is through study of emotion recognition. The data needed for this is readily available and collectable and can be an excellent source for the feedback systems. The challenge of recognition of emotion in speech is convoluted primarily due to the noisy recording condition, the variations in sentiment in sample space and exhibition of multiple emotions in a single sentence. The ambiguity in the labels of training set also increases the complexity of problem addressed. The existing models using probabilistic models have dominated the study but present a flaw in scalability due to statistical inefficiency. The problem of sentiment prediction in spontaneous speech can thus be addressed using a hybrid system comprising of a Convolution Neural Network and Hidden Markov Model.

Keywords


Estimation, Productivity, Deep Neural Networks, CNN, Sentiment Prediction, Spontaneous Speech, HMM.

References