Abstract
Speech has been an integral part of human life acting as one of the five primitive senses of the human body. As such any software or application based upon speech recognition has a high degree of acceptance and a wide range of applications in defense, security, health care, and home automation. Speech is a waffling signal with varying characteristics at a high rate. When examined over a very short scale of time, it can be considered as a stationary signal with very small variations. In this paper, authors have worked upon the detection of a single user using multiple isolated words as speech signals. For designing the system, feature extraction using Mel-frequency cepstral coefficients (MFCCs) and feature matching using dynamic time warping (DTW) are considered as the designing of the system because of its simplicity and efficiency. Short-time spectral analysis is adopted which is the main part of the MFCC algorithm used in feature extraction. To compare any two signals varying in speed or having phase difference between them, DTW is used. Since two spoken words can never be the same, the DTW algorithm is best suited to compare two words.
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Sood, M., Jain, S. (2021). Speech Recognition Employing MFCC and Dynamic Time Warping Algorithm. In: Singh, P.K., Polkowski, Z., Tanwar, S., Pandey, S.K., Matei, G., Pirvu, D. (eds) Innovations in Information and Communication Technologies (IICT-2020). Advances in Science, Technology & Innovation. Springer, Cham. https://doi.org/10.1007/978-3-030-66218-9_27
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DOI: https://doi.org/10.1007/978-3-030-66218-9_27
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