TY - GEN
T1 - Speech enhancement using a state-based transform model
AU - Deisher, Michael E.
AU - Spanias, Andreas
N1 - Funding Information:
This work has been supported by the Mobile Research Council of Intel Corporation. The authors would like to thank Brian Mears of Intel Corporation for his comments and suggestions.
Publisher Copyright:
© 1995 IEEE.
PY - 1994
Y1 - 1994
N2 - An analysis/synthesis technique based on harmonic sinusoidal modeling of speech is used to develop a new hidden Markov model (HMM) based speech enhancement algorithm. State sequence estimation is done using a standard HMM-based approach. State-based enhancement is carried out by assuming a harmonic model for speech, i.e., by representing each block of speech as a sum of sine waves in terms of a set of amplitudes, phases, and harmonically related frequencies. Given the maximum a-posteriori probability (MAP) state sequence, the amplitudes, phases, voicing, and fundamental frequency are estimated. Simulation results are presented, comparing the performance of the proposed algorithm to that of a standard HMM-based approach. The proposed method was found to reduce the structured residual noise normally associated with HMM-based algorithms.
AB - An analysis/synthesis technique based on harmonic sinusoidal modeling of speech is used to develop a new hidden Markov model (HMM) based speech enhancement algorithm. State sequence estimation is done using a standard HMM-based approach. State-based enhancement is carried out by assuming a harmonic model for speech, i.e., by representing each block of speech as a sum of sine waves in terms of a set of amplitudes, phases, and harmonically related frequencies. Given the maximum a-posteriori probability (MAP) state sequence, the amplitudes, phases, voicing, and fundamental frequency are estimated. Simulation results are presented, comparing the performance of the proposed algorithm to that of a standard HMM-based approach. The proposed method was found to reduce the structured residual noise normally associated with HMM-based algorithms.
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U2 - 10.1109/ACSSC.1994.471657
DO - 10.1109/ACSSC.1994.471657
M3 - Conference contribution
AN - SCOPUS:85063502939
T3 - Conference Record - Asilomar Conference on Signals, Systems and Computers
SP - 1242
EP - 1246
BT - Conference Record - 28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994
PB - IEEE Computer Society
T2 - 28th Asilomar Conference on Signals, Systems and Computers, ACSSC 1994
Y2 - 31 October 1994 through 2 November 1994
ER -