Advances in ASR are driven by both scientific achievements in the field and the availability of more powerful hardware. While very powerful CPUs allow us to use ever more complex algorithms in server-based large vocabulary ASR systems (e.g. in telephony applications), the capability of embedded platforms will always lag behind. Nevertheless as the popularity of ASR application grows, we can expect an increasing demand for functionality on embedded platforms as well. For example, replacing simple command and control grammar-based applications by natural language understanding (NLU) systems leads to increased vocabulary sizes and thus the need for greater CPU performance. In this chapter we present an overview of ASR decoder design options with an emphasis on techniques which are suitable for embedded platforms. One needs to keep in mind that there is no one-size-fits-all solution; specific algorithmic improvements may only be best applied to highly restricted applications or scenarios. The optimal solution can usually be achieved by making choices with respect to algorithms aimed at maximizing specific benefits for a particular platform and task.
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Novak, M. (2008). Algorithm Optimizations: Low Computational Complexity. In: Automatic Speech Recognition on Mobile Devices and over Communication Networks. Advances in Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-84800-143-5_10
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