An Introduction to Statistical Spoken Language Understanding
1 Introduction Besides dictation, there are many other practical applications for speech recognition, including command and control, spoken dialog systems [1, 2], speech-to-speech translation [3], and multi-modal interaction [4-6]. The success of these applications relies on the correct recognition of not only what is said, but also what is meant. In contrast to automatic speech recognition (ASR), which converts a speaker's spoken utterance into a text string, spoken language understanding (SLU) consists in interpreting user's intentions from their speech utterances. Traditionally, this has been accomplished by writing context free (CFG) or unification grammars (UG) by hand. The manual grammar authoring process is laborious, expensive and requires a lot of expertise. In recent years, many data-driven models have been proposed for this problem. The main purpose of this overview paper is to provide an introduction to the statistical framework common in spoken language understanding, which has not been widely exposed to signal processing readers in the past. SLU is closely related to natural language understanding (NLU), a field that has been studied for half a century. However, the problem of SLU has its own characteristics. Unlike general domain NLU, SLU is, in the current state of technology, focused only on specific application domains. Hence, many domain-specific constraints can be included in the understanding model. Ostensibly, this may make the problem easier to solve. Unfortunately, spoken language is much noisier than written language. The inputs to a SLU system are not as well-formed as those to an NLU system. They often do not comply with rigid syntactic constraints. Disfluencies such as false starts, repairs, and hesitations are pervasive, especially in conversational speech, and errors made by speech recognizers are inevitable. Therefore, robustness is one of the most important issues in SLU. On the other hand, a robust solution tends to over-generalize and introduce ambiguities, leading to reduction of understanding accuracy. A major challenge to SLU is thus to strike an optimal balance between the robustness and the constraints that prevent over-generalizations and reduce ambiguities. 2 The study of SLU started in the 1970's in the DARPA Speech Understanding Research (SUR) and then the Resource Management (RM) tasks. At this early stage, NLU techniques like finite state machine (FSM) and augmented transition networks (ATNs) were applied for SLU [7]. The study of SLU surged in the 90's, with the DARPA sponsored Air Travel Information System (ATIS) evaluations [8]. Many of the SLU technologies …