Details

Autor(en) / Beteiligte

• Sasou, A.
• Goto, M.
• Hayamizu, S.
• Tanaka, K.

Titel

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

Ist Teil von

• Proceedings of the 2004 14th IEEE Signal Processing Society Workshop Machine Learning for Signal Processing, 2004, 2004, p.295-304

Ort / Verlag

Piscataway NJ: IEEE

Erscheinungsjahr

2004

Link zum Volltext

• IEEE_Xplore

Quelle

IEEE/IET Electronic Library (IEL)

Beschreibungen/Notizen

• 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