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Spoken language understanding system is traditionally designed as a pipelineof a number of components. First, the audio signal is processed by an automaticspeech recognizer for transcription or n-best hypotheses. With the recognitionresults, a natural language understanding system classifies the text tostructured data as domain, intent and slots for down-streaming consumers, suchas dialog system, hands-free applications. These components are usuallydeveloped and optimized independently. In this paper, we present our study onan end-to-end learning system for spoken language understanding. With thisunified approach, we can infer the semantic meaning directly from audiofeatures without the intermediate text representation. This study showed thatthe trained model can achieve reasonable good result and demonstrated that themodel can capture the semantic attention directly from the audio features.

Towards end-to-end spoken language understanding