diff --git a/source/use_case/asr/src/UseCaseHandler.cc b/source/use_case/asr/src/UseCaseHandler.cc
index 7fe959b..850bdc2 100644
--- a/source/use_case/asr/src/UseCaseHandler.cc
+++ b/source/use_case/asr/src/UseCaseHandler.cc
@@ -33,9 +33,9 @@
 namespace app {
 
     /**
-     * @brief           Presents ASR inference results.
-     * @param[in]       results     Vector of ASR classification results to be displayed.
-     * @return          true if successful, false otherwise.
+     * @brief       Presents ASR inference results.
+     * @param[in]   results   Vector of ASR classification results to be displayed.
+     * @return      true if successful, false otherwise.
      **/
     static bool PresentInferenceResult(const std::vector<asr::AsrResult>& results);
 
@@ -63,6 +63,9 @@
             return false;
         }
 
+        TfLiteTensor* inputTensor = model.GetInputTensor(0);
+        TfLiteTensor* outputTensor = model.GetOutputTensor(0);
+
         /* Get input shape. Dimensions of the tensor should have been verified by
          * the callee. */
         TfLiteIntArray* inputShape = model.GetInputShape(0);
@@ -78,19 +81,19 @@
         const float secondsPerSample = (1.0 / audio::Wav2LetterMFCC::ms_defaultSamplingFreq);
 
         /* Set up pre and post-processing objects. */
-        ASRPreProcess preProcess = ASRPreProcess(model.GetInputTensor(0), Wav2LetterModel::ms_numMfccFeatures,
-                inputShape->data[Wav2LetterModel::ms_inputRowsIdx], mfccFrameLen, mfccFrameStride);
+        AsrPreProcess preProcess = AsrPreProcess(inputTensor, Wav2LetterModel::ms_numMfccFeatures,
+                                                 inputShape->data[Wav2LetterModel::ms_inputRowsIdx],
+                                                 mfccFrameLen, mfccFrameStride);
 
         std::vector<ClassificationResult> singleInfResult;
-        const uint32_t outputCtxLen = ASRPostProcess::GetOutputContextLen(model, inputCtxLen);
-        ASRPostProcess postProcess = ASRPostProcess(ctx.Get<AsrClassifier&>("classifier"),
-                model.GetOutputTensor(0), ctx.Get<std::vector<std::string>&>("labels"),
+        const uint32_t outputCtxLen = AsrPostProcess::GetOutputContextLen(model, inputCtxLen);
+        AsrPostProcess postProcess = AsrPostProcess(
+                outputTensor, ctx.Get<AsrClassifier&>("classifier"),
+                ctx.Get<std::vector<std::string>&>("labels"),
                 singleInfResult, outputCtxLen,
                 Wav2LetterModel::ms_blankTokenIdx, Wav2LetterModel::ms_outputRowsIdx
                 );
 
-        UseCaseRunner runner = UseCaseRunner(&preProcess, &postProcess, &model);
-
         /* Loop to process audio clips. */
         do {
             hal_lcd_clear(COLOR_BLACK);
@@ -147,16 +150,20 @@
                      static_cast<size_t>(ceilf(audioDataSlider.FractionalTotalStrides() + 1)));
 
                 /* Run the pre-processing, inference and post-processing. */
-                runner.PreProcess(inferenceWindow, inferenceWindowLen);
-
-                profiler.StartProfiling("Inference");
-                if (!runner.RunInference()) {
+                if (!preProcess.DoPreProcess(inferenceWindow, inferenceWindowLen)) {
+                    printf_err("Pre-processing failed.");
                     return false;
                 }
-                profiler.StopProfiling();
 
+                if (!RunInference(model, profiler)) {
+                    printf_err("Inference failed.");
+                    return false;
+                }
+
+                /* Post processing needs to know if we are on the last audio window. */
                 postProcess.m_lastIteration = !audioDataSlider.HasNext();
-                if (!runner.PostProcess()) {
+                if (!postProcess.DoPostProcess()) {
+                    printf_err("Post-processing failed.");
                     return false;
                 }
 
@@ -166,7 +173,6 @@
                         audioDataSlider.Index(), scoreThreshold));
 
 #if VERIFY_TEST_OUTPUT
-                TfLiteTensor* outputTensor = model.GetOutputTensor(0);
                 armDumpTensor(outputTensor,
                     outputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx]);
 #endif /* VERIFY_TEST_OUTPUT */