Open Access Open Access  Restricted Access Subscription or Fee Access

Survey on Recognition of Repeated Patterns from Speech

A. Kavya, Devaramani Shankar, S. Likitha, J. Gagandeep, Fathima Afroz

Abstract


Extensive research and development has taken place over the last 70 years in the areas of pattern recognition and speech processing. Areas to which these disciplines have been applied include Forensic (e. g., Speaker Identification), Speech Pathology (diagnosis, detection), banking and railway (online announcement), military intelligence (Silent Speech), communications (data compression, speech recognition), and many others. This paper presents a very brief survey of recent developments in repeated pattern recognition using speech processing techniques.


Keywords


Speech Signal Processing, Segmentation, Onset Detection, Pattern Matching,

Full Text:

PDF

References


Chee, L. S., Ai, O. C., & Yaacob, S. (2009, October). Overview of automatic disorder recognition system. In Proc. International Conference on Man-Machine Systems, no. October, Batu Ferringhi, Penang Malaysia (pp. 1-6).

Ravikumar, K. M., Reddy, B., Rajagopal, R., & Nagaraj, H. (2008). Automatic detection of syllable repetition in read speech for objective assessment of disfluent disfluencies. Proceedings of world academy science, engineering and technology, 36, 270-273.

Chee, L. S., Ai, O. C., Hariharan, M., & Yaacob, S. (2009, December). Automatic detection of prolongations and repetitions using LPCC. In 2009 international conference for technical postgraduates (TECHPOS) (pp. 1-4). IEEE.

Lim, S. C., Ooi, C. A., Hariharan, M., & Sazali, Y. (2009). MFCC based recognition of repetitions and prolongations in disfluent speech using k-NN and LDA.

Zhang, J., Dong, B., & Yan, Y. (2013, August). A computer-assist algorithm to detect repetitive disorder automatically. In 2013 International Conference on Asian Language Processing (pp. 249- 252). IEEE.

Mahesha, P., & Vinod, D. S. (2012, June). Automatic classification of dysfluencies in disfluent speech using MFCC. In International Conference on Computing Communication and Information Technology, Chennai.

Palfy, J., & Pospíchal, J. (2011, September). Recognition of repetitions using support vector machines. In Signal Processing Algorithms, Architectures, Arrangements, and Applications SPA 2011 (pp. 1-6). IEEE.

Hariharan, M., Vijean, V., Fook, C. Y., & Yaacob, S. (2012, March). Speech disorder assessment using sample entropy and Least Square Support Vector Machine. In 2012 IEEE 8th International Colloquium on Signal Processing and its Applications (pp. 240-245). IEEE.

Lim, S. C., Ooi, C. A., Hariharan, M., & Sazali, Y. (2009). MFCC based recognition of repetitions and prolongations in disfluent speech using k-NN and LDA.

Waghmare, S. D., Deshmukh, R. R., Shrishrimal, P. P., Waghmare, V. B., Janvale, G. B., & Sonawane, B. (2017). A Comparative Study of Recognition Technique Used for Development of Automatic Disfluent Speech Dysfluency Recognition System. Indian Journal of Science and Technology, 10(21), 1-14.

Chee, L. S., Ai, O. C., Hariharan, M., & Yaacob, S. (2009, December). Automatic detection of prolongations and repetitions using LPCC. In 2009 international conference for technical postgraduates (TECHPOS) (pp. 1-4). IEEE.

Savin, P. S., Ramteke, P. B., & Koolagudi, S. G. (2016). Recognition of repetition and prolongation in disfluent speech using ANN. In Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics (pp. 65-71). Springer, New Delhi.

Ramteke, Pravin B., Shashidhar G. Koolagudi, and Fathima Afroz. "Repetition detection in disfluent speech." In Proceedings of 3rd International Conference on Advanced Computing, Networking and Informatics, pp. 611-617. Springer, New Delhi, 2016.

Ravikumar, K. M., Reddy, B., Rajagopal, R., & Nagaraj, H. (2008). Automatic detection of syllable repetition in read speech for objective assessment of disfluent disfluencies. Proceedings of world academy science, engineering and technology, 36, 270-273.

Ravikumar, K. M., Rajagopal, R., & Nagaraj, H. C. (2009, June). An approach for objective assessment of disfluent speech using MFCC. In The international congress for global science and technology (p. 19).

Afroz, Fathima, and Shashidhar G. Koolagudi. "Recognition and Classification of Pauses in Disfluent Speech Using Acoustic Features." 2019 6th International Conference on Signal Processing and Integrated Networks (SPIN). IEEE, 2019.

Chee, L. S., Ai, O. C., & Yaacob, S. (2009, October). Overview of automatic disorder recognition system. In Proc. International Conference on Man-Machine Systems, no. October, Batu Ferringhi, Penang Malaysia (pp. 1-6).

Nguyen, Q. T. (2016). Speech classification using SIFT features on spectrogram images. Vietnam Journal of Computer Science, 3(4), 247-257.


Refbacks

  • There are currently no refbacks.