Mg. Rahim et Bh. Juang, SIGNAL BIAS REMOVAL BY MAXIMUM-LIKELIHOOD-ESTIMATION FOR ROBUST TELEPHONE SPEECH RECOGNITION, IEEE transactions on speech and audio processing, 4(1), 1996, pp. 19-30
An acoustical mismatch between the training and the testing conditions
of hidden Markov model (HMM)-based speech recognition systems often c
auses a severe degradation in the recognition performance. In telephon
e speech recognition, for example, undesirable signal components due t
o ambient noise and channel distortion, as well as due to different va
riations of telephone handsets render the recognizer unusable for real
-world applications, This paper presents a signal bias removal (SBR) m
ethod based on maximum likelihood estimation for the minimization of t
hese undesirable effects, The proposed method is readily applicable in
various architectures, i.e., discrete (vector-quantization based), se
micontinuous and continuous density HMM, In this paper, the SBR method
, integrated into a discrete density HMM, is applied to telephone spee
ch recognition where the contamination due to extraneous signal compon
ents is assumed to be unknown, To enable real-time implementation, a s
equential method for the estimation of the bias is presented, Experime
ntal results for speaker-independent connected digit recognition show
a reduction in the per digit error rate by up to 41% and 14% during mi
smatched and matched training and testing conditions, respectively.