Difference between revisions of "Hulan-2013-10-11"

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(ASR)
(Dialog system)
 
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* Conducting the initial experiment:
 
* Conducting the initial experiment:
  
#Using 9k dim TF/IDF, compose feature vectors for each query, each answer. Mach the TF/IDF of query+answer to match the TF/IDF of new queries. Add the scores of the Cosine score of the match with queries and answers directly.
+
#Using 9k dim TF/IDF, compose feature vectors for each query, each answer. Mach the TF/IDF of query+answer to match the TF/IDF of new queries.  
  
 
# Keep two top-level categories, try to reduce top-level errors:
 
# Keep two top-level categories, try to reduce top-level errors:
 +
Add the scores of the Cosine score of the match with queries and answers directly:
 +
top-level errors: 2/60 , all errors: 13/60
 +
Use scores of the Cosine score of the match with queries only:
 +
top-level errors: 0/60 , all errors: 7/60
 +
speed: 2 query/second
  
 
Next week:  
 
Next week:  
  
* Reverse index-based fast match
+
* Reverse index-based fast match (only match with queries)
  
 
# code done in python
 
# code done in python
# CER 7/60, speed 1 query/second
+
# CER 7/60, speed 1 query/second  
  
 
* Use the new data set to verify the program.
 
* Use the new data set to verify the program.

Latest revision as of 03:12, 11 October 2013

ASR

ASR Kernel development

[ASR group weekly report]

TTS

  • CD lab files done. Refining the script.
  • Training toolkit is cleaned up. Now no alignment is required. Parallel training is done.
  • Tried syllable based system instead of phones.
  • Collected an online-novel reading.

Next week:

  • Refine the script
  • Clean up the online reading.

Dialog system

  • Conducting the initial experiment:
  1. Using 9k dim TF/IDF, compose feature vectors for each query, each answer. Mach the TF/IDF of query+answer to match the TF/IDF of new queries.
  1. Keep two top-level categories, try to reduce top-level errors:
Add the scores of the Cosine score of the match with queries and answers directly:
top-level errors: 2/60 , all errors: 13/60
Use scores of the Cosine score of the match with queries only:
top-level errors: 0/60 , all errors: 7/60
speed: 2 query/second

Next week:

  • Reverse index-based fast match (only match with queries)
  1. code done in python
  2. CER 7/60, speed 1 query/second
  • Use the new data set to verify the program.