Abstract
The speech is the most famous and essential method of communication among persons. The communication between human being and machine is called human PC interface. Speech has the capability of being the most essential method of communication with PC. Speech is also an acoustically rich flag that gives impressive individual data about talkers. The statement of emotions in speech sounds and relating capacities to visualize such emotions are both essential parts of human–machine communication. Discoveries from ponders trying to portray the acoustic properties of emotions present in vocal. The speech shows that discourse acoustics give an outside signal to the level of nonspecific excitement related with passionate procedures and, to a lesser degree, the relative charm of experienced feelings. A brief overview on bifurcation of speech, either into single or series of words with persistent or unconstrained speech is also taken into consideration within this paper. This paper represents the methodology that converts the vocal signals into corresponding text. As soon as the signal is converted into text, then the same will be analyzed with the sequential as well as with the parallel sum algorithm works for the parallel random access machine. Using both algorithms, the mood of the speech will be identified as romantic, sad or rock. Comparative studies of sequential and parallel approaches are also discussed in this paper. This paper concludes that the time complexity of the sequential executed algorithm can be reduced to a particular extent by using the parallel approaches.
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Acknowledgements
The author would like to thank the Department of Computer Science and Engineering, Indian Institute of Technology (ISM), Dhanbad, Jharkhand, India for providing a platform and all necessary requirement for generating the dataset used in this study. This dataset has been created by the author in the lab. The authors are also thankful to the organizer to provide a nice platform for presenting the research.
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Agarwal, G., Maheshkar, V., Maheshkar, S., Gupta, S. (2019). Vocal Mood Recognition: Text Dependent Sequential and Parallel Approach. In: Malik, H., Srivastava, S., Sood, Y., Ahmad, A. (eds) Applications of Artificial Intelligence Techniques in Engineering. Advances in Intelligent Systems and Computing, vol 698. Springer, Singapore. https://doi.org/10.1007/978-981-13-1819-1_14
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DOI: https://doi.org/10.1007/978-981-13-1819-1_14
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