Title :
Hidden Markov model with noise-adaptive codebook for noisy speech recognition
Author :
Wang Shuying ; Wu Shanpei
Author_Institution :
Beijing Univ. of Posts & Telecommun., China
Abstract :
At present, many researchers turn their attention to automatic speech recognition in noisy environments. The main reason is that speech recognizers trained in quiet conditions but operated in a noisy environment usually have poor performance. We use discrete the hidden Markov model with noise-adaptive codebook for noisy speech recognition. The goal is to improve the recognition accuracy of recognizer in a noisy environment. When testing with noise-contaminated utterances at an SNR of 20 dB, the system has a recognition accuracy of 35%, by using the noise-adaptive codebook, the system has an accuracy of up to 90%.<>
Keywords :
hidden Markov models; noise; speech coding; speech recognition; 20 dB; SNR; automatic speech recognition; hidden Markov model; noise environment; noise-adaptive codebook; noise-contaminated utterances; noisy speech recognition; recognition accuracy; Acoustic noise; Acoustic testing; Autocorrelation; Automatic speech recognition; Degradation; Hidden Markov models; Speech enhancement; Speech recognition; Vectors; Working environment noise;
Conference_Titel :
TENCON '93. Proceedings. Computer, Communication, Control and Power Engineering.1993 IEEE Region 10 Conference on
Conference_Location :
Beijing, China
Print_ISBN :
0-7803-1233-3
DOI :
10.1109/TENCON.1993.327988