Abstract：A Gaussian mixture model-hidden Markov model (GMM-HMM) for speech recognition uses the most likely state sequence (MLSS) criterion to get the best state series of observations. Since the MLSS search algorithm only considers the maximum likelihood state of speech frame, the effects of other suboptimal states are neglected and some important information is lost, which reduces the system recognition rate. Acoustic state likelihood modelling and supervised state modelling are used here to better utilize the acoustic state likelihood information. A state likelihood cluster feature and a supervised state feature are used to calculate the state likelihood of the acoustic feature Mel frequency cepstrum coefficient (MFCC). Tests show that these three features improve the speech recognition accuracy. The state likelihood cluster and supervised state feature reduce the relative error rate by 6.10% and 9.66% for isolated word recognition compared to GMM-HMM using only MFCC and by 2.53% and 11.05% for continuous speech recognition.
肖熙, 徐晨. 基于声学状态似然值得分模型及监督状态模型的语音识别特征融合算法[J]. 清华大学学报（自然科学版）, 2019, 59(6): 476-481.
XIAO Xi, XU Chen. Speech feature fusion algorithm based on acoustic state likelihood and supervised state modelling. Journal of Tsinghua University(Science and Technology), 2019, 59(6): 476-481.
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2019, Vol. 59 
