Mathematical analysis

Programming language

Speech processing

Philosophy

Artificial intelligence

Dialog box

Representation (politics)

Acoustic model

Audio mining

Spoken language

Natural language understanding

Mathematics

Domain (mathematical analysis)

Pipeline (software)

Politics

Political science

Transcription (linguistics)

Computer science

World Wide Web

Natural language

Linguistics

Language model

Natural language processing

Speech recognition

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.

Towards End-to-end Spoken Language Understanding