1. Deep Exploration and Filtering of Text (DEFT)
Denys Katerenchuk
Speech Lab,
Queens College, CUNY

2. DEFT DARPA, Department of Defense Excess of data
Important data may be implicit
Efficiency
Goal:
Develop automated deep NLP technology for efficient information processing and understanding

3. DEFT
“Overwhelmed by deadlines and the sheer volume of available foreign intelligence, analysts may miss crucial links, especially when meaning is deliberately concealed or otherwise obfuscated”
Bonnie Dorr, DARPA program manager

4. Overview Introduction Innovation Algorithms and Tools Results
Future work

5. Introduction NLP has a range of tools to discover target data
Information Retrieval
Automated Speech Recognition
Machine Translation
Etc.
Robust combination is needed!

6. Information-Rich Relational Analysis for Spoken Data
Innovation
Information-Rich Relational Analysis for Spoken Data

7. Hypothesis
Combine rich speech representation and annotations with prosodic features to improve F- measure score

8. Motivation Speech is different from text
Speech may contain disfluency and depends on ASR performance
Named Entities are often OOV
Prosodic information is ignored

9. ASR Hypothesis Representation
Lattice CN

10. Lattice vs CN CN Much smaller in size and less processing time
Better align (improve WER)
Contain posterior probabilities
Lattice
Contain complete hypothesis

11. Automatic Speech Recognition
KALDI (
Powerful open source tool for ASR
Written in C++ and can be easily modified
CN and Lattice support
Complete recipes for some common corpora
Supports Grid Engine

12. Named Entity Recognition
Blender Name Tagger
Simple to use
Works with ACE data (corpus)
Using MALLET Machine Learning toolkit

13. Prosody Analysis AuToBI The must tool for extraction prosodic features
No need for introduction

14. Algorithm Speech AuToBI KALDI (ASR) Prosodic features Text New
Representation
Clusters
Blender Name
Tagger (NER)
Model
Blender Name
Tagger (NER)

15. ASR Results KALDI Training time ~ 78 hours WSJ Models Triphone
SGMM – (better, but not ready yet)
1 best Triphone Recognizer performance: % (WER)

16. Results

17. Future work Improve current ASR model Improve current CN approach
Add n-best and oracle models
Extend feature set
Event Recognition

18. Thank you!