Abstract
In this paper, the turning point algorithm has applied on the input speech signal from International Phonetic Alphabet database in the form of vowels, consonants and narratives of American-English. From this algorithm, the input speech signal is compressed by reducing the sampling rate by half of the input sampling rate. After that, the compressed speech signal is played back. The compressed speech signal now has far much better hearing quality as compared to the hearing quality of the input speech signal. This observation is true for all the categories of the input speech signals taken into consideration judged on the basis of both MOS and Average MOS. Finally, the compression performance is also computed for all the category of the speech signals.
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Abenstein, J. P., & Tompkins, W. J. (1982). A new data reduction algorithm for real-time ECG analysis. IEEE Transactions on Biomedical Engineering, BME-29(1), 43–48.
Akdeniz, R., & Yarman, S. (2005). A novel method to represent speech signals. Signal Processing, 85, 37–50.
Bera, S. C., Chakrabarty, B., & Ray, J. K. (2005). A mathematical model for analysis for ECG waves in a normal subject. Measurement, 38, 53–60.
Cetin, A. E., & Koymen, H. (2000). Compression of digital biomedical signals. In J. D. Bronzino (Ed.), The biomedical engineering handbook. Boca Raton: CRC Press. Chap. 54.
Chen, W., Hsieh, L., & Yuan, S. (2004). High performance data compression method with pattern matching for biomedical ECG and arterial pulse waveforms. Computer Methods and Programs in Biomedicine, 74, 11–27.
International Phonetic Association (1999). Handbook of the International Phonetic Association, a guide to the use of IPA. Cambridge: Cambridge University Press.
Jalaleddine, S. M. S., Hutchens, C. G., Strattan, R. D., & Coberly, W. A. (1990). ECG data compression techniques—a unified approach. IEEE Transactions on Biomedical Engineering, 37(4), 329–343.
Kumar, V., Saxena, S. C., & Giri, V. K. (2006). Direct data compression of ECG signal for telemedicine. International Journal of Systems Science, 37(1), 45–63.
Manikandan, M. S., & Dandapat, S. (2008). Wavelet threshold based TDL and TDR algorithms for real-time ECG signal compression. Biomedical Signal Processing and Control, 3, 44–66.
Motamedi, J. A., Djahanshahi, H., & Movahedian, H. (1994). Determining the proper compression algorithm for biomedical signals and design of an optimum graphic system to display them. Journal of Engineering, 7(2), 119–123.
Padma, T., Latha, M. M., & Ahmed, A. (2009). ECG compression and labview implementation. Journal of Biomedical Science and Engineering, 2, 177–183.
Proakis, J. G., & Manolakis, D. G. (2004). Digital signal processing principles, algorithms, and applications (3rd ed.). New Delhi: Prentice Hall of India.
Rabiner, L. R., & Schafer, R. W. (2009). Digital processing of speech signals. New Delhi: Pearson Education.
Rabiner, L., Juang, B., & Yegnanarayana, B. (2009). Fundamental of speech recognition. New Delhi: Pearson Education.
Salomon, D. (2011). Data compression. The complete reference. New Delhi: Springer International Edition.
Sayood, K. (2006). Introduction to data compression. San Francisco: Morgan Kaufmann.
Tan, L. (2008). Digital signal processing fundamentals and applications. San Diego: Academic Press.
Tompkin, W. J. (2006). Biomedical digital signal processing. New Delhi: Prentice Hall of India.
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Arif, M., Anand, R.S. Turning point algorithm for speech signal compression. Int J Speech Technol 15, 513–522 (2012). https://doi.org/10.1007/s10772-012-9151-7
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DOI: https://doi.org/10.1007/s10772-012-9151-7