The week's pick
Reseach Article
Theoretical Survey of the Formant Tracking Algorithm
| 2nd National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2013) |
| Foundation of Computer Science USA |
| NCIPET - Number 2 |
| March 2012 |
| Authors: Manisha H. Awatade |
| 5514c2aa-4918-4447-914d-7053230197af |
Manisha H. Awatade . Theoretical Survey of the Formant Tracking Algorithm. 2nd National Conference on Innovative Paradigms in Engineering and Technology (NCIPET 2013). NCIPET, 2 (March 2012), 14-17.
Abstract
The formant is the important part of the phonetic characters, and reliable formant tracking algorithm is the base to study the phonetics. Based on the development course of the phonetic formant tracking algorithm, the linear prediction coding (LPC) and the model matching method are introduced emphatically, and there own advantages and disadvantages are analyzed, and the model matching method based on the hidden dynamic model will be the development direction of the future formant tracking technology.
References
- S.S.McCandless. 1974. An algorithm for automatic formant extraction using linear prediction spectra. IEEE Transactions on Acoustics, Speech, and Signal Processing. No.22(2). p.135-141.
- D.J.Broad, F.Clermont. 1989. Formant estimation by linear transformation of the LPC cepstrum. Journal of the Acoustical Society of America. No.86(5). p.2013-2017.
- A.M.De Lima Araujo, F.Violaro. (1998). Formant frequency estimation using a mel scale LPC algorithm. Telecommunications Symposium, ITS „98 Proceedings. Vol.1. p.207-212.
- D.Talkin. 1987. Speech formant trajectory estimation using dynamic programming with modulated transition costs. Journal Acoustical Society of America. No.82(S1). p.S55.
- G..Kopec. 1986. Formant Tracking Using Hidden Markov Models and Vector Quantization. IEEE Transactions on Acoustics, Speech, and Signal processing. No.34(4). p.709-729.
- Lee, M.; vanSanten, J.; Mobius, B.; Olive, J.; 2005 Formant tracking using context dependent phonemic information IEEE transaction on speech and audio processing vol.13.p.741-750.
- J.K.Baker. 1975. The Dragon System-An overview. IEEE Transactions of Acoustics, Speech, and Signal Processing. No.23(1). p.24-29..
- P.Zolfaghari, T.Robinson. 1996. Formant analysis using mixtures of Gaussians. Proc. ICSLP. Vol.2. p.1229-1232.
- J.Darch, B.Milner, S.Xu, S.Vaseghi and Y.Qin. 2005. Predicting formant frequencies from MFCC vectors. Proc. ICASSP. Vol.1. p.941-944.
- M.Lee, J.vanSanten, B.Mobius, J.Olive. 2005. Formant Tracking Using Context-Dependent Phonemic Information. IEEE Transactions on Speech and Audio Processing. No.13(5). p.741-750.
- D.T.Toledano, J.G..Villardebo, L.H.Gomez. 2006. Initialization, training, and context-dependency in HMM-based formant tracking. IEEE Transactions on Acoustics, Speech, and Signal processing. No.14(2). p.511-522.
- L.Deng. 1998. A dynamic, feature-based approach to the interface between phonology and phonetics for speech modeling and recognition. Speech Communication. No.24(4). p.299-323.
- H.B.Richards, J.S.Bridle. 1999. The HDM: A segmental hidden dynamic model of coarticulation. Proc. ICASSP. Vol.1. p.357-360.
- L.Deng, J. Ma. 2000. Spontaneous speech recognition using a statistical coarticulatory model for the vocal-tract-resonance dynamic. Journal of the Acoustical Society of America. No.108(6). p.3036-3048.
- L.Deng, L.J.Lee, H.Attias, A.Acero. 2007. Adaptive kalman filtering and smoothing for tracking vocal resonances using a continuous-valued hidden dynamic model. IEEE Transactions on Audio, Speech, and Language processing. No.15(1). p.13-23.
- L.Deng, A.Acero, I.Bazzi. 2006. Tracking vocal tract resonances using a quantized nonlinear function embedded in a temporal constraint. IEEE Transactions on Acoustics, Speech, and Language processing. No.14(2). p.425-434.
- Yanli.Zheng, M. Hasegawa-Johnson. 2004. Formant tracking by mixture state particle filter. Proc. ICASSP. Vol.1. p.565-568.
Index Terms
Keywords