Hidden Markov model with noise-adaptive codebook for noisy speech recognition

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%.<>