Connectionist Speech Recognition: A Hybrid Approach
Connectionist Speech Recognition: A Hybrid Approach
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发表时间：
1993-10
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通讯作者：
H. Bourlard;N. Morgan
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作者：
H. Bourlard;N. Morgan
来自出版商：《联结主义语音识别：混合方法》描述了一种方法的理论和实现，该方法将神经网络方法纳入基于隐马尔可夫模型 (HMM) 的最先进的连续语音识别系统中，以提高其性能。在此框架中，神经网络（特别是多层感知器或 MLP）仅限于整个系统的明确定义的子任务，即 HMM 发射概率估计和特征提取。这本书描述了作者之间为期五年的成功国际合作。吸取的经验教训形成了一个案例研究，展示了如何开发混合系统以将神经网络与更传统的统计方法结合起来。本书阐述了统计系统框架中神经网络的优点和局限性。使用标准数据库并与一些传统方法进行比较，结果表明 MLP 概率估计可以提高识别性能。讨论了其他方法，尽管这些方法没有明确的实验结果。连接主义语音识别：混合方法适用于任何打算使用神经网络进行语音识别或在现有成功统计方法提供的框架内使用的人。这包括在语音识别领域工作的研究和开发小组，包括标准方法和神经网络方法，以及其他模式识别和/或神经网络研究人员。本书也适合作为神经网络或语音处理高级课程的教材。
From the Publisher: Connectionist Speech Recognition: A Hybrid Approach describes the theory and implementation of a method to incorporate neural network approaches into state-of-the-art continuous speech recognition systems based on Hidden Markov Models (HMMs) to improve their performance. In this framework, neural networks (and in particular, multilayer perceptrons or MLPs) have been restricted to well-defined subtasks of the whole system, i.e., HMM emission probability estimation and feature extraction. The book describes a successful five year international collaboration between the authors. The lessons learned form a case study that demonstrates how hybrid systems can be developed to combine neural networks with more traditional statistical approaches. The book illustrates both the advantages and limitations of neural networks in the framework of a statistical system. Using standard databases and comparing with some conventional approaches, it is shown that MLP probability estimation can improve recognition performance. Other approaches are discussed, though there is no such unequivocal experimental result for these methods. Connectionist Speech Recognition: A Hybrid Approach is of use to anyone intending to use neural networks for speech recognition or within the framework provided by an existing successful statistical approach. This includes research and development groups working in the field of speech recognition, both with standard and neural network approaches, as well as other pattern recognition and/or neural network researchers. This book is also suitable as a text for advanced courses on neural networks or speech processing.