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diff --git a/tensorflow/contrib/lite/models/speech_hotword_model_test.cc b/tensorflow/contrib/lite/models/speech_hotword_model_test.cc
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+++ b/tensorflow/contrib/lite/models/speech_hotword_model_test.cc
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+/* Copyright 2017 The TensorFlow Authors. All Rights Reserved.
+
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+==============================================================================*/
+// Unit test for speech Hotword model using TFLite Ops.
+
+#include <string.h>
+
+#include <memory>
+#include <string>
+
+#include "base/logging.h"
+#include "file/base/path.h"
+#include "testing/base/public/googletest.h"
+#include <gtest/gtest.h>
+#include "tensorflow/contrib/lite/context.h"
+#include "tensorflow/contrib/lite/interpreter.h"
+#include "tensorflow/contrib/lite/kernels/register.h"
+#include "tensorflow/contrib/lite/model.h"
+#include "tensorflow/contrib/lite/models/test_utils.h"
+
+namespace tflite {
+namespace models {
+
+void RunTest(int model_input_tensor, int svdf_layer_state_tensor,
+             int model_output_tensor, const string& model_name,
+             const string& golden_in_name, const string& golden_out_name) {
+  // Read the model.
+  string tflite_file_path = file::JoinPath(TestDataPath(), model_name);
+  auto model = FlatBufferModel::BuildFromFile(tflite_file_path.c_str());
+  CHECK(model) << "Failed to read model from file " << tflite_file_path;
+
+  // Initialize the interpreter.
+  ops::builtin::BuiltinOpResolver builtins;
+  std::unique_ptr<Interpreter> interpreter;
+  InterpreterBuilder(*model, builtins)(&interpreter);
+  CHECK(interpreter != nullptr);
+  interpreter->AllocateTensors();
+
+  // Reset the SVDF layer state.
+  memset(interpreter->tensor(svdf_layer_state_tensor)->data.raw, 0,
+         interpreter->tensor(svdf_layer_state_tensor)->bytes);
+
+  // Load the input frames.
+  Frames input_frames;
+  const string input_file_path = file::JoinPath(TestDataPath(), golden_in_name);
+  ReadFrames(input_file_path, &input_frames);
+
+  // Load the golden output results.
+  Frames output_frames;
+  const string output_file_path =
+      file::JoinPath(TestDataPath(), golden_out_name);
+  ReadFrames(output_file_path, &output_frames);
+
+  const int speech_batch_size =
+      interpreter->tensor(model_input_tensor)->dims->data[0];
+  const int speech_input_size =
+      interpreter->tensor(model_input_tensor)->dims->data[1];
+  const int speech_output_size =
+      interpreter->tensor(model_output_tensor)->dims->data[1];
+  const int input_sequence_size =
+      input_frames[0].size() / (speech_input_size * speech_batch_size);
+  float* input_ptr = interpreter->tensor(model_input_tensor)->data.f;
+  float* output_ptr = interpreter->tensor(model_output_tensor)->data.f;
+
+  // The first layer (SVDF) input size is 40 (speech_input_size). Each speech
+  // input frames for this model is 1280 floats, which can be fed to input in a
+  // sequence of size 32 (input_sequence_size).
+  for (int i = 0; i < TestInputSize(input_frames); i++) {
+    int frame_ptr = 0;
+    for (int s = 0; s < input_sequence_size; s++) {
+      for (int k = 0; k < speech_input_size * speech_batch_size; k++) {
+        input_ptr[k] = input_frames[i][frame_ptr++];
+      }
+      interpreter->Invoke();
+    }
+    // After the whole frame (1280 floats) is fed, we can check the output frame
+    // matches with the golden output frame.
+    for (int k = 0; k < speech_output_size; k++) {
+      ASSERT_NEAR(output_ptr[k], output_frames[i][k], 1e-5);
+    }
+  }
+}
+
+TEST(SpeechHotword, OkGoogleTestRank1) {
+  constexpr int kModelInputTensor = 0;
+  constexpr int kSvdfLayerStateTensor = 4;
+  constexpr int kModelOutputTensor = 18;
+
+  RunTest(kModelInputTensor, kSvdfLayerStateTensor, kModelOutputTensor,
+          "speech_hotword_model_rank1.tflite", "speech_hotword_model_in.csv",
+          "speech_hotword_model_out_rank1.csv");
+}
+
+TEST(SpeechHotword, OkGoogleTestRank2) {
+  constexpr int kModelInputTensor = 17;
+  constexpr int kSvdfLayerStateTensor = 1;
+  constexpr int kModelOutputTensor = 18;
+  RunTest(kModelInputTensor, kSvdfLayerStateTensor, kModelOutputTensor,
+          "speech_hotword_model_rank2.tflite", "speech_hotword_model_in.csv",
+          "speech_hotword_model_out_rank2.csv");
+}
+
+}  // namespace models
+}  // namespace tflite