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Robust speech recognition and feature extraction using HMM2

机译：使用HMM2进行可靠的语音识别和特征提取

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This paper presents the theoretical basis and preliminary experimental results of a new HMM model. referred to as HMM2, which can be considered as a mixture of HMMs. In this new model, the emission probabilities of the temporal (primary) HMM are estimated through secondary, state specific, HMMs working in the acoustic feature space. Thus, while the primary HMM is performing the usual time warping and integration, the secondary HMMs are responsible for extracting/modeling the possible feature dependencies, while performing frequency warping and integration. Such a model has several potential advantages, such as a more flexible modeling of the time/frequency structure of the speech signal. When working with spectral features, such a system can also perform nonlinear spectral warping, effectively implementing a form of nonlinear vocal tract normalization. Furthermore, it will be shown that HMM2 can be used to extract noise robust features, supposed to be related to formant regions, which can be used as extra features for traditional HMM recognizers to improve their performance. These issues are evaluated in the present paper, and different experimental results are reported on the Numbers95 database.
机译：本文介绍了一种新的HMM模型的理论基础和初步的实验结果。称为HMM2，可以视为HMM的混合物。在这个新模型中，通过在声学特征空间中工作的特定于状态的次要HMM估算了时间（主要）HMM的发射概率。因此，当主要HMM执行通常的时间扭曲和积分时，次要HMM负责在执行频率扭曲和积分的同时提取/建模可能的特征依赖性。这样的模型具有几个潜在的优点，例如对语音信号的时间/频率结构的更灵活的建模。当使用频谱特征时，这样的系统还可以执行非线性频谱扭曲，从而有效地实现非线性声道标准化的一种形式。此外，将显示HMM2可用于提取与共振峰区域有关的噪声鲁棒特征，这些特征可用作传统HMM识别器的额外特征，以改善其性能。本文对这些问题进行了评估，并在Numbers95数据库中报告了不同的实验结果。

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