Reducing mismatch between HMMs trained with clean speech and speech signals recorded under background noise can be approached by distribution adaptation using parallel model combination (PMC). Accurate PMC has no closed-form expression, therefore simplification assumptions must be made in implementation. Under a new log-max assumption, adaptation formula for log-spectral parameters are presented, both for static and dynamic parameters. The system takes the mean vector (41) made up of the static part (41a) and the dynamic part (41b) and the noise vector (43) made up of the static part (43a) and dynamic part (43b) and applies to a decision circuit (45) to determine if the quiet vector plus the gain of speech produced in noise with respect to clean speech is greater than the noisy mean vector and if so the static part is equal to the gain plus the clean speech vector and the dynamic part is the change in the the quiet speech vector and if not greater than the noise vector then the static part equals the noise vector and the dynamic part is zero.
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机译：通过使用并行模型组合（PMC）进行分布自适应，可以减少使用干净语音训练的HMM与记录在背景噪声下的语音信号之间的失配。精确的PMC没有封闭形式的表达式，因此在实现时必须做出简化的假设。在新的对数最大值假设下，给出了对数谱参数的自适应公式，包括静态和动态参数。系统采用由静态部分（ 41 a ）和动态部分（ 41 < / B> b ）和由静态部分（ 43 a ）组成的噪声矢量（ 43 ）和动态部分（ 43 b ），并应用于决策电路（ 45 ），以确定静默矢量加上语音增益是否产生于相对于干净语音的噪声大于噪声平均矢量，如果是，则静态部分等于增益加上干净语音矢量，而动态部分是静默语音向量中的变化，并且如果不大于噪声矢量那么静态部分等于噪声矢量，动态部分等于零。
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