This chapter presents a new formulation that tightly integrates the detection – based algorithm into the maximum a posteriori (MAP) decision. The key to this formulation is to implement the sequential detection algorithm and to recurrently apply the sequential probability ratio test in a time – synchronous, single – pass decoding framework. The chapter shows that realizing the detection – based recognition in single – pass architecture is feasible. It provides an overview of the mathematical foundation of this approach, serving as an introduction to the general detection – based approach for computer processing of speech and language. This overview starts with the conventional fixed – sample – size detection, which then naturally extends to sequential detection theory. Finally, it presents a comprehensive case study on how the sequential detection technique is successfully applied to a speech understanding task that is related to personal information management.<\/p>\n","protected":false},"excerpt":{"rendered":"
This chapter presents a new formulation that tightly integrates the detection – based algorithm into the maximum a posteriori (MAP) decision. The key to this formulation is to implement the sequential detection algorithm and to recurrently apply the sequential probability ratio test in a time – synchronous, single – pass decoding framework. 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