Spoken language understanding (SLU) is an emerging field in between the areas of speech processing and natural language processing. The term spoken language understanding has largely been coined for targeted understanding of human speech directed at machines. This project covers our research on SLU tasks such as domain detection, intent determination, and slot filling, using data-driven methods.
- Deeper Understanding: Moving beyond shallow targeted understanding towards building domain independent SLU models.
- Scaling SLU: Quickly bootstrapping SLU models without any manual effort using multiple information sources, including semantic knowledge graph, search query click logs, and structured and unstructured web documents.
- Deep Learning for SLU: Using deep neural networks (DNNs) for various SLU tasks.
- Cross-Lingual Understanding: Developing techniques to address the language expansion for building cross lingual SLU models, exploiting machine translation technologies.
- SLU for Multi-Human Machine: Extending single human/machine SLU setup towards multi-human/machine conversations.