diff --git a/modules/audio_processing/agc2/rnn_vad/rnn_unittest.cc b/modules/audio_processing/agc2/rnn_vad/rnn_unittest.cc
index 2e920e8d80..c311b55edc 100644
--- a/modules/audio_processing/agc2/rnn_vad/rnn_unittest.cc
+++ b/modules/audio_processing/agc2/rnn_vad/rnn_unittest.cc
@@ -30,6 +30,21 @@ namespace rnn_vad {
 namespace test {
 namespace {
 
+constexpr std::array<float, kFeatureVectorSize> kFeatures = {
+    -1.00131f,   -0.627069f, -7.81097f,  7.86285f,    -2.87145f,  3.32365f,
+    -0.653161f,  0.529839f,  -0.425307f, 0.25583f,    0.235094f,  0.230527f,
+    -0.144687f,  0.182785f,  0.57102f,   0.125039f,   0.479482f,  -0.0255439f,
+    -0.0073141f, -0.147346f, -0.217106f, -0.0846906f, -8.34943f,  3.09065f,
+    1.42628f,    -0.85235f,  -0.220207f, -0.811163f,  2.09032f,   -2.01425f,
+    -0.690268f,  -0.925327f, -0.541354f, 0.58455f,    -0.606726f, -0.0372358f,
+    0.565991f,   0.435854f,  0.420812f,  0.162198f,   -2.13f,     10.0089f};
+
+void WarmUpRnnVad(RnnBasedVad& rnn_vad) {
+  for (int i = 0; i < 10; ++i) {
+    rnn_vad.ComputeVadProbability(kFeatures, /*is_silence=*/false);
+  }
+}
+
 void TestFullyConnectedLayer(FullyConnectedLayer* fc,
                              rtc::ArrayView<const float> input_vector,
                              rtc::ArrayView<const float> expected_output) {
@@ -220,6 +235,37 @@ INSTANTIATE_TEST_SUITE_P(
       return info.param.ToString();
     });
 
+// Checks that the speech probability is zero with silence.
+TEST(RnnVadTest, CheckZeroProbabilityWithSilence) {
+  RnnBasedVad rnn_vad(GetAvailableCpuFeatures());
+  WarmUpRnnVad(rnn_vad);
+  EXPECT_EQ(rnn_vad.ComputeVadProbability(kFeatures, /*is_silence=*/true), 0.f);
+}
+
+// Checks that the same output is produced after reset given the same input
+// sequence.
+TEST(RnnVadTest, CheckRnnVadReset) {
+  RnnBasedVad rnn_vad(GetAvailableCpuFeatures());
+  WarmUpRnnVad(rnn_vad);
+  float pre = rnn_vad.ComputeVadProbability(kFeatures, /*is_silence=*/false);
+  rnn_vad.Reset();
+  WarmUpRnnVad(rnn_vad);
+  float post = rnn_vad.ComputeVadProbability(kFeatures, /*is_silence=*/false);
+  EXPECT_EQ(pre, post);
+}
+
+// Checks that the same output is produced after silence is observed given the
+// same input sequence.
+TEST(RnnVadTest, CheckRnnVadSilence) {
+  RnnBasedVad rnn_vad(GetAvailableCpuFeatures());
+  WarmUpRnnVad(rnn_vad);
+  float pre = rnn_vad.ComputeVadProbability(kFeatures, /*is_silence=*/false);
+  rnn_vad.ComputeVadProbability(kFeatures, /*is_silence=*/true);
+  WarmUpRnnVad(rnn_vad);
+  float post = rnn_vad.ComputeVadProbability(kFeatures, /*is_silence=*/false);
+  EXPECT_EQ(pre, post);
+}
+
 }  // namespace
 }  // namespace test
 }  // namespace rnn_vad