计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 172-174.doi: 10.11896/jsjkx.200200006
张经, 杨健, 苏鹏
ZHANG Jing, YANG Jian, SU Peng
摘要： 声学模型建模可实现对语音信号的处理和特征抽取,是语音识别过程中必不可少的基础性工作,同时也是影响语音识别整体性能的一个重要因素。在语音识别中,选择合适的建模基元能使后续系统获得更高的准确率和更强的鲁棒性。音节是汉语等汉藏语系的最小发音单位,针对其发音特点,研究使用音节作为汉藏语系语音识别的建模基元,再提取相应的特征进行识别就有着尤为重要的意义。针对单音节识别目前的研究进展,首先介绍了基于有限状态矢量量化的算法,以及其改进算法在单音节识别中的研究成果;然后介绍了基于隐马尔可夫模型的算法,并详细介绍了将隐马尔可夫模型与其他算法相结合的音节识别研究成果;接着介绍了基于神经网络的算法;最后总结并提出了单音节识别研究未来发展的重要方向。
中图分类号:
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