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A hybrid SVM/DDBHMM decision fusion modeling for robust continuous digital speech recognition

机译：鲁棒的连续数字语音识别的混合SVM / DDBHMM决策融合建模

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This paper proposes an improved hybrid support vector machine and duration distribution based hidden Markov (SVM/DDBHMM) decision fusion model for robust continuous digital speech recognition. We investigate the probability outputs combination of support vector machine and Gaussian mixture model in pattern recognition (called FSVM),and embed the fusion probability as similarity into the phone state level decision space of our duration distribution based hidden Markov model (DDBHMM) speech recognition system (named FSVM/DDBHMM). The performances of FSVM and FSVM/DDBHMM are demonstrated in Iris database and continuous mandarin digital speech corpus in 4 noise environments (white, volvo, babble and destroyerengine) from NOISEX-92. The experimental results show the effectiveness of FSVM in Iris data, and the improvement of average word error rate reduction of FSVM/DDBHMM from 6% to 20% compared with the DDBHMM baseline at various signal noise ratios (SNRs) from -5 dB to 30 dB by step of 5 dB.
机译：本文提出了一种改进的混合支持向量机和基于持续时间分布的隐马尔可夫（SVM / DDBHMM）决策融合模型，用于鲁棒连续数字语音识别。我们研究了支持向量机和高斯混合模型在模式识别（称为FSVM）中的概率输出组合，并将融合概率作为相似性嵌入到我们基于持续时间分布的隐马尔可夫模型（DDBHMM）语音识别系统的电话状态级别决策空间中（名为FSVM / DDBHMM）。 FSVM和FSVM / DDBHMM的性能在Iris数据库和NOISEX-92在4种噪声环境（白色，沃尔沃，ba不休和毁灭性引擎）中的连续普通话数字语音语料库中得到了证明。实验结果表明FSVM在虹膜数据中的有效性，并且在从-5 dB到30 dB的各种信号噪声比下，与DDBHMM基线相比，FSVM / DDBHMM的平均单词错误率降低了6％至20％分贝为5 dB。

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