Frame-wise HMM adaptation using state-dependent reverberation estimates
Frame-wise HMM adaptation using state-dependent reverberation estimates
复制标题
使用状态相关混响估计进行逐帧 HMM 自适应
DOI：
10.1109/icassp.2011.5947600
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发表时间：
2011
期刊：
影响因子：
--
通讯作者：
Walter Kellermann
中科院分区：
文献类型：
--
作者：
A. Sehr;R. Maas;Walter Kellermann
提出了一种新的用于混响鲁棒远距离说话语音识别的逐帧模型自适应方法。它调整静态倒谱特征的手段，以捕获混响特征向量序列的统计数据从远程通话语音记录。在解码期间，使用通过联合使用特征域混响模型和最佳部分状态序列确定的后期混响的状态相关估计来适配延迟的手段。由于可以完全独立地估计障碍和混响模型的参数，因此该方法对于变化的声学环境是非常灵活的。由于逐帧模型自适应，一些HMM的限制被解除，并获得超过匹配混响训练的识别结果，在适度增加解码复杂度的代价。
A novel frame-wise model adaptation approach for reverberation-robust distant-talking speech recognition is proposed. It adjusts the means of static cepstral features to capture the statistics of reverberant feature vector sequences obtained from distant-talking speech recordings. The means of the HMMs are adapted during decoding using a state-dependent estimate of the late reverberation determined by joint use of a feature-domain reverberation model and optimum partial state sequences. Since the parameters of the HMMs and the reverberation model can be estimated completely independently, the approach is very flexible with respect to changing acoustic environments. Due to the frame-wise model adaptation, some of the HMM limitations are relieved, and recognition results surpassing that of matched reverberant training are obtained at the cost of a moderately increased decoding complexity.