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Automatic speech recognition (ASR) is a central and common component of voice-driven information processing systems in human language technology, including spoken language translation (SLT), spoken language understanding (SLU), voice search, spoken document retrieval, and so on. Interfacing ASR with its downstream text-based processing tasks of translation, understanding, and information retrieval (IR) creates both challenges and opportunities in optimal design of the combined, speech-enabled systems. We present an optimization-oriented statistical framework for the overall system design where the interactions between the subsystems in tandem are fully incorporated and where design consistency is established between the optimization objectives and the end-to-end system performance metrics. Techniques for optimizing such objectives in both the decoding and learning phases of the speech-centric information processing (SCIP) system design are described, in which the uncertainty in speech recognition subsystem's outputs is fully considered and marginalized. This paper provides an overview of the past and current work in this area. Future challenges and new opportunities are also discussed and analyzed.
He et al. (Wed,) studied this question.