Comparison of auto-regressive, non-stationary excited signal parameter estimation methods

Previously, we proposed an auto-regressive hidden Markov model (AR-HMM) and an accompanying parameter estimation method. An AR-HMM was obtained by combining an AR process with an HMM introduced as a non-stationary excitation model. We demonstrated that the AR-HMM can accurately estimate the characte...

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Bibliographic Details
Main Authors: Sasou, A., Goto, M., Hayamizu, S., Tanaka, K.
Format: Conference Proceeding
Language:English
Subjects:
Acoustic signal processing
Acoustics
Biological and medical sciences
Ear and associated structures. Auditory pathways and centers. Hearing. Vocal organ. Phonation. Sound production. Echolocation
Exact sciences and technology
Fundamental and applied biological sciences. Psychology
Fundamental areas of phenomenology (including applications)
Hidden Markov models
Information science
Libraries
Parameter estimation
Physics
Signal analysis
Signal resolution
Speech analysis
Speech enhancement
Speech processing
Vertebrates: nervous system and sense organs
White noise
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Summary:Previously, we proposed an auto-regressive hidden Markov model (AR-HMM) and an accompanying parameter estimation method. An AR-HMM was obtained by combining an AR process with an HMM introduced as a non-stationary excitation model. We demonstrated that the AR-HMM can accurately estimate the characteristics of both articulatory systems and excitation signals from high-pitched speech. As the parameter estimation method iteratively executes learning processes of HMM parameters, the proposed method was calculation-intensive. Here, we propose two novel kinds of auto-regressive, non-stationary excited signal parameter estimation methods to reduce the amount of calculation required
ISSN:1551-2541
2378-928X
DOI:10.1109/MLSP.2004.1422987