  Spoken language understanding system is traditionally designed as a pipeline
of a number of components. First, the audio signal is processed by an automatic
speech recognizer for transcription or n-best hypotheses. With the recognition
results, a natural language understanding system classifies the text to
structured data as domain, intent and slots for down-streaming consumers, such
as dialog system, hands-free applications. These components are usually
developed and optimized independently. In this paper, we present our study on
an end-to-end learning system for spoken language understanding. With this
unified approach, we can infer the semantic meaning directly from audio
features without the intermediate text representation. This study showed that
the trained model can achieve reasonable good result and demonstrated that the
model can capture the semantic attention directly from the audio features.
