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README.md

@@ -13,16 +13,11 @@ Voice Activity Detection (VAD) Test Dataset
 
 This dataset is based on the `test.clean` and `test.other` splits from the
 [librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr)
-corpus. It includes two binary features:
+corpus. It includes two binary labels:
 
 - **speech**: Indicates presence of speech ([0, 1]), computed using a dynamic threshold method with background noise estimation and smoothing.
 
-- **confidence**: A post-processing flag to optionally correct transient dropouts in speech. It is set to 1 by default, but switches to 0 for up to ~0.1 seconds (3 chunks of audio) following a transition from speech to silence.  Approximately 7% of the `speech` features in this dataset are `confidence` 0.  The remaining 93% are `confidence` 1 enabling VAD testing.
-
-| split      | active speech, hours | confidence, % |
-| ---------- | :------------------: | :-----------: |
-| test.clean | 5.4                  | 93.2          |
-| test.other | 5.3                  | 92.6          |
+- **confidence**: A post-processing flag to optionally correct transient dropouts in speech. It is set to 1 by default, but switches to 0 for up to ~0.1 seconds (3 chunks of audio) following a transition from speech to silence.  Approximately 7% of the `speech` labels in this dataset are `confidence` 0.  The remaining 93% are `confidence` 1 enabling VAD testing.
 
 The dataset has minimal background noise, making it suitable for mixing with
 external noise samples to test VAD robustness.
@@ -66,17 +61,20 @@ In practice you would run the AUC computation across the entire test split.
 
 ## Ignore transient dropouts
 
-Filtering out low-confidence frames (~7% of the data) improves precision and
-reduces false positives, helping account for slower VAD responses in real-world
-applications.
+The `confidence` values can be used to filter the data. Removing zero confidence
+values excludes 6.8% of the dataset and causes numerical increase in
+computed precision.  This compensates for slower moving voice activity decisions
+as encountered in real-world applications.
 
 ```console
 confidence = dataset["test.clean"][0]["confidence"]
-confidence_mask = (np.array(confidence) == 1)
+
+speech_array = np.array(speech)
+speech_probs_array = np.array(speech_probs)
 
 roc_auc_confidence = roc_auc_score(
-    np.array(speech)[confidence_mask],
-    np.array(speech_probs)[confidence_mask],
+    speech_array[np.array(confidence) == 1],
+    speech_probs_array[np.array(confidence) == 1],
 )
 ```
 
@@ -89,14 +87,11 @@ model with `test.clean` split.
 <img src="assets/roc_test_clean.png" alt="Example from test.clean with Silero-VAD"/>
 
 Precision values are increased when data is sliced by `confidence` values.
-These low-confidence `speech` features are flagged rather than removed, allowing
+These low-confidence `speech` labels are flagged rather than removed, allowing
 users to either exclude them (as shown here) or handle them with other methods.
 
 <img src="assets/roc_test_clean_exclude_low_confidence.png" alt="Example from test.clean with Silero-VAD"/>
 
-Code to generate these plots is available on
-[GitHub](https://github.com/guynich/vad_eval_curves).
-
 # License Information
 
 This derivative dataset retains the same license as the source dataset
@@ -107,7 +102,7 @@ This derivative dataset retains the same license as the source dataset
 
 # Citation Information
 
-Features contributed by Guy Nicholson were added to the following dataset.
+Labels contributed by Guy Nicholson were added to the following dataset.
 ```
 @inproceedings{panayotov2015librispeech,
   title={Librispeech: an ASR corpus based on public domain audio books},