TY - GEN
T1 - Unsupervised adaptation without estimated transriptions
AU - Lee, Hyeopwoo
AU - Yook, Dongsuk
PY - 2013/10/18
Y1 - 2013/10/18
N2 - To estimate the unknown distortion parameters from input test signals, estimated transcriptions are typically used for unsupervised adaptation. In a low signal to noise ratio (SNR) condition, the transcription estimated by a decoding procedure can be error prone because of the high mismatch between the acoustic models and the input signal. As a result, it can cause performance degradation of the adapted systems. To account for this problem, we propose an unsupervised adaptation method that can adapt the acoustic models without the estimated transcription. Instead, Gaussian mixture models (GMM) and pseudo phoneme models (PPM) are used. Using these models the unknown distortion parameters are estimated based on the vector Taylor series (VTS) model adaptation scheme. On the Aurora2 task, we obtained relative reduction of 5.4% in word error rate (WER).
AB - To estimate the unknown distortion parameters from input test signals, estimated transcriptions are typically used for unsupervised adaptation. In a low signal to noise ratio (SNR) condition, the transcription estimated by a decoding procedure can be error prone because of the high mismatch between the acoustic models and the input signal. As a result, it can cause performance degradation of the adapted systems. To account for this problem, we propose an unsupervised adaptation method that can adapt the acoustic models without the estimated transcription. Instead, Gaussian mixture models (GMM) and pseudo phoneme models (PPM) are used. Using these models the unknown distortion parameters are estimated based on the vector Taylor series (VTS) model adaptation scheme. On the Aurora2 task, we obtained relative reduction of 5.4% in word error rate (WER).
KW - Unsupervised adaptation
KW - robust speech recognition
KW - vector Taylor series
UR - http://www.scopus.com/inward/record.url?scp=84890443262&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84890443262&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2013.6639206
DO - 10.1109/ICASSP.2013.6639206
M3 - Conference contribution
AN - SCOPUS:84890443262
SN - 9781479903566
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 7918
EP - 7921
BT - 2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
T2 - 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Y2 - 26 May 2013 through 31 May 2013
ER -