论文标题
改善不绑定状态绑定的商品混合HMM声学建模
Improving Factored Hybrid HMM Acoustic Modeling without State Tying
论文作者
论文摘要
在这项工作中，我们表明，定义了一个有生物的混合隐藏的马尔可夫模型（FH-HMM），而没有任何语音状态趋势的表现优于最先进的混合HMM。分量的混合HMM以模型（标签）上下文的方式提供了与传感器模型的链接，同时保留了混合HMM方法的声学和语言模型的严格分离。此外，我们表明可以从划痕中训练分解的混合模型，而无需在任何训练步骤中使用语音状态键。我们的建模方法可实现TripHone的环境，同时避免通过语音上下文中的局部标准化的局部标准化后的后验，以避免语音状态趋势。为机板300h和LibrisPeech提供了实验结果。在前者的任务中，我们还表明，与标准的混合型HMM相比，与基于分类和回归树（CART）的语音状态趋势相比，训练期间的分类混合动力可以更好地利用训练期间的正则化技术。
In this work, we show that a factored hybrid hidden Markov model (FH-HMM) which is defined without any phonetic state-tying outperforms a state-of-the-art hybrid HMM. The factored hybrid HMM provides a link to transducer models in the way it models phonetic (label) context while preserving the strict separation of acoustic and language model of the hybrid HMM approach. Furthermore, we show that the factored hybrid model can be trained from scratch without using phonetic state-tying in any of the training steps. Our modeling approach enables triphone context while avoiding phonetic state-tying by a decomposition into locally normalized factored posteriors for monophones/HMM states in phoneme context. Experimental results are provided for Switchboard 300h and LibriSpeech. On the former task we also show that by avoiding the phonetic state-tying step, the factored hybrid can take better advantage of regularization techniques during training, compared to the standard hybrid HMM with phonetic state-tying based on classification and regression trees (CART).
