Answers.Body: <p>Regarding DCF, Carbonell et al. (2007) state &quot;Although it is an excellent evaluation tool, the DCF has the limitation that it has parameters that imply a particular application of the speaker detection technology.&quot;</p>
  <p>For EER the authors make the following statement: &quot;The EER is a concise summary of the discrimination capability of the detector. As such it is a very powerful indicator of the discrimination ability of the detector, across a wide range of applications. However, it does not measure calibration, the ability to set good decision thresholds.&quot;</p>
  <p><strong>References</strong></p>
  <p>Carbonell, J. G., Siekmann, J., Müller, C., van Leeuwen, D. A., &amp; Brümmer, N. (2007). An Introduction to Application-Independent Evaluation of Speaker Recognition Systems. In Speaker Classification I (pp. 330–353).</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-05-24T09:51:54.850Z
Answers.LastActivityDate: 2021-05-24T09:51:54.850Z
Answers.UserDisplayName: Brian O'Donnell
Answers.Body: <p>DCF might be more suitable</p>
  <p>EER is a metric used to evaluate the performance of a binary classification system, such as speaker verification or biometric authentication. It represents the point on the ROC curve where the false acceptance rate (FAR) and false rejection rate (FRR) are equal.</p>
  <p>DFC considers the costs associated with false alarms (false positives) and missed detections (false negatives). The goal is to minimize the DCF, and it is usually evaluated at different probability thresholds.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2023-07-25T10:41:25.020Z
Answers.LastActivityDate: 2023-07-25T10:46:57.590Z
Answers.LastEditDate: 2023-07-25T10:46:57.590Z
Answers.UserDisplayName: Nirmal Sankalana
Body: <p>I was designing a multi-speaker identification model, so I searched for some metrics that one may use. I found two metrics:</p>
  <ol>
  <li>EER (equal error rate)</li>
  <li>DCF (detection cost function)</li>
  </ol>
  <p>What is the difference between them? Is one better than the other for my model?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-06-04T02:39:24.420Z
Comments.Text: It's probably a good idea to provide a reference to the resources where you found these two metrics and briefly describe what "multi-speaker identification model" is, for people not familiar with this task (like me).
Comments.UserDisplayName: nbro
Tags: <machine-learning><comparison><metric><speech-recognition>
Title: What is the difference between the "equal error rate" and "detection cost function" metrics?
UserDisplayName: Kais Hasan
ThreadText: # What is the difference between the "equal error rate" and "detection cost function" metrics?
  
  (Asked by: username_0 on 2021-05-24)
  
  I was designing a multi-speaker identification model, so I searched for some metrics that one may use. I found two metrics:
  
  1. EER (equal error rate)
  2. DCF (detection cost function)
  
  What is the difference between them? Is one better than the other for my model?
  
  **Tags:** machine-learning, comparison, metric, speech-recognition
  > **Comments:**
  > **username_1**: It's probably a good idea to provide a reference to the resources where you found these two metrics and briefly describe what "multi-speaker identification model" is, for people not familiar with this task (like me).  
  
  ---
  
  ## 2 answers
  
  ### Answer by username_2 on 2021-05-24. Score: 0
  
  Regarding DCF, Carbonell et al. (2007) state "Although it is an excellent evaluation tool, the DCF has the limitation that it has parameters that imply a particular application of the speaker detection technology."
  
  For EER the authors make the following statement: "The EER is a concise summary of the discrimination capability of the detector. As such it is a very powerful indicator of the discrimination ability of the detector, across a wide range of applications. However, it does not measure calibration, the ability to set good decision thresholds."
  
  **References**
  
  Carbonell, J. G., Siekmann, J., Müller, C., van Leeuwen, D. A., & Brümmer, N. (2007). An Introduction to Application-Independent Evaluation of Speaker Recognition Systems. In Speaker Classification I (pp. 330–353).
  
  
  ---
  
  ### Answer by username_3 on 2023-07-25. Score: 0
  
  DCF might be more suitable
  
  EER is a metric used to evaluate the performance of a binary classification system, such as speaker verification or biometric authentication. It represents the point on the ROC curve where the false acceptance rate (FAR) and false rejection rate (FRR) are equal.
  
  DFC considers the costs associated with false alarms (false positives) and missed detections (false negatives). The goal is to minimize the DCF, and it is usually evaluated at different probability thresholds.
  
  
  ---
  
  