Decoding optimal state sequence with smooth state likelihoods
Decoding optimal state sequence with smooth state likelihoods
复制标题
用平滑状态似然解码最优状态序列
DOI：
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发表时间：
1996
期刊：
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通讯作者：
I. Zeljkovic
中科院分区：
文献类型：
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作者：
I. Zeljkovic
提出了一种新的算法，允许隐马尔可夫模型（HMM）的状态序列的解码，同时约束状态的似然性是更均匀的。在基于隐马尔可夫模型的语音识别器中，解码的最优状态序列受到隐马尔可夫模型拓扑结构和语法的限制。因此，由维特比算法导出的最可能的状态序列可以受到具有非常高的可能性的少数状态的影响-通常导致识别错误。本文提出了一种通过引入与特定时间帧的当前状态似然性和最高状态似然性之差成比例的惩罚来解码具有较少波动状态概率的状态序列的方法。这些惩罚被添加到维特比前向路径在每个时间帧的累积似然。这种技术，被称为平滑状态似然解码算法（SSLDA），大大降低了识别错误率的连接数字测试上进行的两个语音数据库来自现场试验。对于可变长度的数字串，在一个数据库上的错误率降低了40%以上，在另一个田间试验数据库上的错误率降低了60%以上。
A novel algorithm that allows the decoding of hidden Markov model (HMM) state sequences while constraining the state likelihoods to be more uniform is presented. In HMM-based speech recognizers, the decoded optimal state sequence is restricted by the HMM topology and the grammar. Thus, the most likely state sequence derived by the Viterbi algorithm can be influenced by a few states with very high likelihoods-often resulting in recognition errors. This paper presents a method for decoding state sequences with less volatile state probabilities by introducing penalties proportional to the difference of the current state likelihood and the highest state likelihood for the particular time frame. These penalties are added to the cumulative likelihoods in the Viterbi forward path at every time frame. This technique, referred to as the smooth state likelihood decoding algorithm (SSLDA), reduced recognition error-rates substantially on connected digit tests performed on two speech databases derived from field trials. The error rate was reduced by more than 40% on the one database and more than 60% on the other field trial database for variable length digit strings.