一种基于多模型和集成决策方法的鲁棒语音识别算法

Shengxi Pan, Jia Liu, Jintao Jiang, Zuoying Wang, Dajin Lu
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引用次数: 0

摘要

提出了一种新的多模型集成决策鲁棒语音识别算法。提出了一种并行mmiid (pmmiid)算法。该算法可以将不同模型的优点集成到一个系统中。该算法基于DDBHMM(基于持续时间分布的隐马尔可夫模型)[2],同时使用不同的声学模型。这些模型包括通道错配校正(CMC)模型、多备选语音模型、汉语普通话语音的声调和非声调模型、语音活动检测(VAD)模型和状态跳过模型。在恶劣环境下,多模型系统的语音识别精度优于单模型系统。实验结果表明,该识别系统的错误率为2.9%,与单模型的基线系统相比降低了81%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel robust speech recognition algorithm based on multi-models and integrated decision method
In this paper, a new robust speech recognition algorithm of multi-models and integrated decision(MMID) is proposed. A parallel MMID(PMMID) algorithm is developed. By using this new algorithm the advantages of different models can be integrated into one system. This algorithm uses different acoustic models at the same time based on DDBHMM (duration distribution based Hidden Markov Model)[2]. These different models include the channel-mismatch-correct(CMC) model, more-alternative-pronunciation model, tone and non-tone models of Chinese Mandarin speech, voice activity detection(VAD) model and state-skip model. The speech recognition accuracy of the multi-model system is better than that of single-model system in the adverse environments. The experimental results show that the error rate of the recognition system is 2.9% and reduced by 81% compared with the baseline system of the single-model.
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