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README.md

@@ -12,11 +12,12 @@ task_categories:
 Voice Activity Detection (VAD) Test Dataset
 
 This dataset is based on the `test.clean` and `test.other` splits from the
-librispeech_asr corpus. It includes two binary features:
+[librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr)
+corpus. It includes two binary features:
 
 - **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 correct transient dropouts in speech. It is set to 1 by default, but switches to 0 for up to ~0.1 seconds (3 frames) following a transition from speech to silence.
+- **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.
 
 The dataset has minimal background noise, making it suitable for mixing with
 external noise samples to test VAD robustness.
@@ -28,17 +29,17 @@ A plot for an example showing audio samples and the `speech` feature.
 
 <img src="assets/test_other_item_02.png" alt="Example from test.other"/>
 
-The example below shows brief zero blips in the `speech` feature during natural
-short pauses. Since some VAD models may react more slowly, the `confidence`
-feature offers a way to optionally ignore these blips when evaluating
-performance.
+The example below shows brief dropouts in the `speech` feature during natural
+short pauses of quiet in the talker's speech.  Since some VAD models may react
+more slowly, the `confidence` feature offers a way to optionally ignore these
+transient droputs when evaluating performance.
 
 <img src="assets/test_clean_item_02.png" alt="Example from test.other"/>
 
 # Example usage of dataset
 
-The model under test must support processing a chunk size of 512 audio samples
-at 16000 Hz generating a prediction for each `speech` feature.
+The VAD model under test must support processing a chunk size of 512 audio
+samples at 16000 Hz generating a prediction for each `speech` feature.
 
 ```console
 import datasets
@@ -50,17 +51,20 @@ dataset = datasets.load_dataset("guynich/librispeech_asr_test_vad")
 audio = dataset["test.clean"][0]["audio"]["array"]
 speech = dataset["test.clean"][0]["speech"]
 
-# Compute probabilities from model under test (block size 512).
-speech_probs = model_under_test(audio)
+# Compute voice activity probabilities
+speech_probs = vad_model(audio)
 
-# Add test code here such as AUC metrics.
-# In practice you would run this across the entire test split.
+# Add test code here
 roc_auc = roc_auc_score(speech, speech_probs)
 ```
+In practice you would run the AUC computation across the entire test split.
 
-The `confidence` values can also be used to filter the data. Removing
-low-confidence frames excludes about 6.8% of the dataset and helps improve
-precision when evaluating VAD performance.
+## Ignore transient dropouts
+
+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"]
@@ -82,20 +86,23 @@ 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.
+Precision values are increased when data is sliced by `confidence` values.
+These low-confidence `speech` features 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"/>
 
 # License Information
 
-This dataset retains the same license as the source dataset.
+This derivative dataset retains the same license as the source dataset
+[librispeech_asr](https://huggingface.co/datasets/openslr/librispeech_asr).
 
 [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/)
 
 
 # Citation Information
 
-Additional features contributed by Guy Nicholson for the following dataset.
+Features contributed by Guy Nicholson were added to the following dataset.
 ```
 @inproceedings{panayotov2015librispeech,
   title={Librispeech: an ASR corpus based on public domain audio books},