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README.md

@@ -9,22 +9,18 @@ task_categories:
 - text-classification
 ---
 
-# librispeech_asr_test_vad
+Voice Activity Detection (VAD) Test Dataset
 
-A dataset for testing voice activity detection (VAD).
+This dataset is based on the `test.clean` and `test.other` splits from the
+librispeech_asr corpus. It includes two binary features:
 
-This dataset uses test splits [`test.clean`, `test.other`] extracted
-from the
-[`librispeech_asr` dataset](https://huggingface.co/datasets/openslr/librispeech_asr).
+- **speech**: Indicates presence of speech ([0, 1]), computed using a dynamic threshold method with background noise estimation and smoothing.
 
-There are two additional features.
+- **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.
 
-1. Binary classification of speech activity, called `speech`.  These binary values [0, 1] were computed from speech audio samples using a dynamic threshold method with background noise estimation and smoothing.
+The dataset has minimal background noise, making it suitable for mixing with
+external noise samples to test VAD robustness.
 
-2. Binary classification of confidence, called `confidence`.  These binary values [0, 1] are computed as follows.  The default confidence is 1.  After a `speech` transition from 1 to 0 the confidence is set to 0 up to a maximum of three 0s in `speech` (approximately 0.1 second).  This can be used to correct for temporary blips in the `speech` feature and unknown decay in the method under test.
-
-This test dataset has little background noise thus enables mixing with noise
-samples to assess voice activity detection robustness.
 
 ## Example data
 
@@ -32,11 +28,10 @@ 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 following example demonstrates short zero blips in the `speech` feature for
-valid short pauses in the talker's speech.  However a VAD model under test may
-have slower reaction time.  The `confidence` feature provides an optional means
-for reducing the impact of these short zero blips when computing metrics for a
-method under test.
+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.
 
 <img src="assets/test_clean_item_02.png" alt="Example from test.other"/>
 
@@ -63,9 +58,10 @@ speech_probs = model_under_test(audio)
 roc_auc = roc_auc_score(speech, speech_probs)
 ```
 
-The confidence values can be used to slice the data.  This removes 6.8% of the
-entire dataset `speech` features and removing these low confidence values
-increases precision.
+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.
+
 ```console
 confidence = dataset["test.clean"][0]["confidence"]