Abstract
The air microphone used in communication devices to acquire speech signal gathers highly imperceptible signal in noisy background conditions. Bone conducted speech signal appears to be a promising tool to avoid this situation and improve the quality of communication between two users because of its inherent capability of attenuating high frequency signals. Though, there is no background noise present, the quality of extracted bone conducted signal is usually quite low in terms of intelligibility and strength. The reason for this quality degradation can again be accounted to the high frequency signal repulsion nature of bones. To rectify this issue and to make the bone conducted signal useful in communication systems, some signal processing schemes are required to be developed. This paper introduces application of two signal processing schemes which are very commonly used in speech recognition systems, Linear Predictive Coding (LPC) and MFCC (Mel Frequency Cepstral Coefficient), to enhance the bone conducted signal and shows comparison between them. Results of the analysis show that slight improvement in noise reduction is possible by using the proposed techniques. However, retrieval of lost information, due to bone conduction of speech, cannot be achieved by any of the two proposed techniques and a more robust scheme has to be developed for bone conducted signal improvement.
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Singh, P., Mukul, M.K., Prasad, R. (2018). Bone Conducted Speech Signal Enhancement Using LPC and MFCC. In: Tiwary, U. (eds) Intelligent Human Computer Interaction. IHCI 2018. Lecture Notes in Computer Science(), vol 11278. Springer, Cham. https://doi.org/10.1007/978-3-030-04021-5_14
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DOI: https://doi.org/10.1007/978-3-030-04021-5_14
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