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Modeling a class posterior probability of context dependent phonemes in a speech recognition system
Modeling a class posterior probability of context dependent phonemes in a speech recognition system
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机译：在语音识别系统中模拟上下文相关音素的一类后验概率
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摘要
What is disclosed is a system and method for modelling a class posterior probability of context dependent phonemes in a speech recognition system. A representation network is trained by projecting a N-dimensional feature vector into G intermediate layers of nodes. At least some features are associated with a class label vector. A last intermediate layer ZG of the representation network is discretized to obtain a discretized layer {circumflex over (Z)}. Feature vector Q is obtained by randomly selecting V features from discretized layer {circumflex over (Z)}. Q is repeatedly hashed to obtain a vector Qf where Qf is an output of the fth hashing. An equivalent scalar representation is determined for each Qf. In a manner more fully disclosed herein, a posterior probability Pf is determined for each (x, b) pair based on the equivalent scalar representation of each respective Qf. The obtained posterior probabilities are used to improve classification accuracy in a speech recognition system.
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机译：公开了一种用于对语音识别系统中的上下文相关音素的类后验概率建模的系统和方法。通过将N维特征向量投影到节点的G个中间层中来训练表示网络。至少一些特征与类标签向量相关联。将表示网络的最后一个中间层Z G 离散化，以获得离散化层。通过从离散层中随机选择V个特征来获得特征向量Q。反复对Q进行哈希处理以获得向量Q f ，其中Q f 是第f 次哈希处理的输出。为每个Q f 确定一个等效的标量表示形式。以本文更全面公开的方式，基于每个相应的Q f 的等量标量表示，为每个（x，b）对确定后验概率P f 。所获得的后验概率用于提高语音识别系统中的分类准确性。
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