“09-30 Lantian Li”版本间的差异

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1. To go on studying a scoring method on GMM-UBM aiming to design a cohort reference speaker models.
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Weekly Summary
  
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1). Implement the K-means algorithem to cluster the training set in order for organizing the cohort set.
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1. On the basis of testing on a small scale, a total-batch experiment was made.
  
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2). Make the verfication score results dividied into four parts. --"Real True Speaker"/"Sensitive True
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The result for "true speaker" shows that there exists a score gap between the real-speaker and cohort
  
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Speaker"/"Sensitive Imp Speaker"/"Abosulte Imp Speaker".
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speaker. Besides, the variance from the real-true-speaker is larger than the sen-true-speaker based on the
  
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3). Re-score for the four parts on the cohort set.
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cohort set. For imposter-speaker having the similar result, However, it does not meet the original hypothesis
  
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4). Score ranking for each part and draw score-rank distrubution diagrams.
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that if the speaker is not the true speaker, it may have the similar scoring between this hypothesis
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speaker model and corhort models. But experimental result shows that there still has a gap.
  
 
Next Week
 
Next Week
  
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1. Go on the task1 to explore the inherent law of re-scoring results and use the cohort set to
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1. Need to analyse the experimental results especiallly for the imposter-speaker. And to find whether the
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clustering algorithm is right.
  
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reduce the error rate on the "Sensitive True Speaker"/"Sensitive Imp Speaker".
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2. Read i-vector paper and I want to study the i-vector based soeaker recognition system.

2014年9月29日 (一) 02:37的最后版本

Weekly Summary

1. On the basis of testing on a small scale, a total-batch experiment was made.

The result for "true speaker" shows that there exists a score gap between the real-speaker and cohort

speaker. Besides, the variance from the real-true-speaker is larger than the sen-true-speaker based on the

cohort set. For imposter-speaker having the similar result, However, it does not meet the original hypothesis

that if the speaker is not the true speaker, it may have the similar scoring between this hypothesis

speaker model and corhort models. But experimental result shows that there still has a gap.

Next Week

1. Need to analyse the experimental results especiallly for the imposter-speaker. And to find whether the

clustering algorithm is right.

2. Read i-vector paper and I want to study the i-vector based soeaker recognition system.

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