For large vocabulary continuous speech recognition, speech decoders treat time sequence with context information using large probabilistic models. The software of such speech decoders tend to be large and complex since it has to handle both relationships of its component functions and timing of computation at the same time. In the traditional signal processing area such as measurement and system control, block diagram based implementations are common where systems are designed by connecting blocks of components. The connections describe flow of signals and this framework greatly helps to understand and design complex systems. In this research, we show that speech decoders can be effectively decomposed to diagrams or pipelines. Once they are decomposed to pipelines, they can be easily implemented in a highly abstracted manner using a pure functional programming language with delayed evaluation. Based on this perspective, we have re-designed our pure-functional decoder Husky proposing a new design paradigm for speech recognition systems. In the evaluation experiments, it is shown that it efficiently works for a large vocabulary continuous speech recognition task.
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