diff --git a/source/use_case/asr/src/AsrClassifier.cc b/source/use_case/asr/src/AsrClassifier.cc
index 84e66b7..4ba8c7b 100644
--- a/source/use_case/asr/src/AsrClassifier.cc
+++ b/source/use_case/asr/src/AsrClassifier.cc
@@ -20,117 +20,125 @@
 #include "TensorFlowLiteMicro.hpp"
 #include "Wav2LetterModel.hpp"
 
-template<typename T>
-bool arm::app::AsrClassifier::GetTopResults(TfLiteTensor* tensor,
-                                            std::vector<ClassificationResult>& vecResults,
-                                            const std::vector <std::string>& labels, double scale, double zeroPoint)
-{
-    const uint32_t nElems = tensor->dims->data[arm::app::Wav2LetterModel::ms_outputRowsIdx];
-    const uint32_t nLetters = tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx];
+namespace arm {
+namespace app {
 
-    if (nLetters != labels.size()) {
-        printf("Output size doesn't match the labels' size\n");
-        return false;
-    }
+    template<typename T>
+    bool AsrClassifier::GetTopResults(TfLiteTensor* tensor,
+                                      std::vector<ClassificationResult>& vecResults,
+                                      const std::vector <std::string>& labels, double scale, double zeroPoint)
+    {
+        const uint32_t nElems = tensor->dims->data[Wav2LetterModel::ms_outputRowsIdx];
+        const uint32_t nLetters = tensor->dims->data[Wav2LetterModel::ms_outputColsIdx];
 
-    /* NOTE: tensor's size verification against labels should be
-     *       checked by the calling/public function. */
-    if (nLetters < 1) {
-        return false;
-    }
-
-    /* Final results' container. */
-    vecResults = std::vector<ClassificationResult>(nElems);
-
-    T* tensorData = tflite::GetTensorData<T>(tensor);
-
-    /* Get the top 1 results. */
-    for (uint32_t i = 0, row = 0; i < nElems; ++i, row+=nLetters) {
-        std::pair<T, uint32_t> top_1 = std::make_pair(tensorData[row + 0], 0);
-
-        for (uint32_t j = 1; j < nLetters; ++j) {
-            if (top_1.first < tensorData[row + j]) {
-                top_1.first = tensorData[row + j];
-                top_1.second = j;
-            }
-        }
-
-        double score = static_cast<int> (top_1.first);
-        vecResults[i].m_normalisedVal = scale * (score - zeroPoint);
-        vecResults[i].m_label = labels[top_1.second];
-        vecResults[i].m_labelIdx = top_1.second;
-    }
-
-    return true;
-}
-template bool arm::app::AsrClassifier::GetTopResults<uint8_t>(TfLiteTensor* tensor,
-                                                              std::vector<ClassificationResult>& vecResults,
-                                                              const std::vector <std::string>& labels, double scale, double zeroPoint);
-template bool arm::app::AsrClassifier::GetTopResults<int8_t>(TfLiteTensor* tensor,
-                                                             std::vector<ClassificationResult>& vecResults,
-                                                             const std::vector <std::string>& labels, double scale, double zeroPoint);
-
-bool arm::app::AsrClassifier::GetClassificationResults(
-            TfLiteTensor* outputTensor,
-            std::vector<ClassificationResult>& vecResults,
-            const std::vector <std::string>& labels, uint32_t topNCount, bool use_softmax)
-{
-        UNUSED(use_softmax);
-        vecResults.clear();
-
-        constexpr int minTensorDims = static_cast<int>(
-            (arm::app::Wav2LetterModel::ms_outputRowsIdx > arm::app::Wav2LetterModel::ms_outputColsIdx)?
-             arm::app::Wav2LetterModel::ms_outputRowsIdx : arm::app::Wav2LetterModel::ms_outputColsIdx);
-
-        constexpr uint32_t outColsIdx = arm::app::Wav2LetterModel::ms_outputColsIdx;
-
-        /* Sanity checks. */
-        if (outputTensor == nullptr) {
-            printf_err("Output vector is null pointer.\n");
-            return false;
-        } else if (outputTensor->dims->size < minTensorDims) {
-            printf_err("Output tensor expected to be %dD\n", minTensorDims);
-            return false;
-        } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) < topNCount) {
-            printf_err("Output vectors are smaller than %" PRIu32 "\n", topNCount);
-            return false;
-        } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) != labels.size()) {
+        if (nLetters != labels.size()) {
             printf("Output size doesn't match the labels' size\n");
             return false;
         }
 
-        if (topNCount != 1) {
-            warn("TopNCount value ignored in this implementation\n");
-        }
-
-        /* To return the floating point values, we need quantization parameters. */
-        QuantParams quantParams = GetTensorQuantParams(outputTensor);
-
-        bool resultState;
-
-        switch (outputTensor->type) {
-            case kTfLiteUInt8:
-                resultState = this->GetTopResults<uint8_t>(
-                        outputTensor, vecResults,
-                        labels, quantParams.scale,
-                        quantParams.offset);
-                break;
-            case kTfLiteInt8:
-                resultState = this->GetTopResults<int8_t>(
-                        outputTensor, vecResults,
-                        labels, quantParams.scale,
-                        quantParams.offset);
-                break;
-            default:
-                printf_err("Tensor type %s not supported by classifier\n",
-                    TfLiteTypeGetName(outputTensor->type));
-                return false;
-        }
-
-        if (!resultState) {
-            printf_err("Failed to get sorted set\n");
+        /* NOTE: tensor's size verification against labels should be
+         *       checked by the calling/public function. */
+        if (nLetters < 1) {
             return false;
         }
 
+        /* Final results' container. */
+        vecResults = std::vector<ClassificationResult>(nElems);
+
+        T* tensorData = tflite::GetTensorData<T>(tensor);
+
+        /* Get the top 1 results. */
+        for (uint32_t i = 0, row = 0; i < nElems; ++i, row+=nLetters) {
+            std::pair<T, uint32_t> top_1 = std::make_pair(tensorData[row + 0], 0);
+
+            for (uint32_t j = 1; j < nLetters; ++j) {
+                if (top_1.first < tensorData[row + j]) {
+                    top_1.first = tensorData[row + j];
+                    top_1.second = j;
+                }
+            }
+
+            double score = static_cast<int> (top_1.first);
+            vecResults[i].m_normalisedVal = scale * (score - zeroPoint);
+            vecResults[i].m_label = labels[top_1.second];
+            vecResults[i].m_labelIdx = top_1.second;
+        }
+
         return true;
-}
\ No newline at end of file
+    }
+    template bool AsrClassifier::GetTopResults<uint8_t>(TfLiteTensor* tensor,
+                                                        std::vector<ClassificationResult>& vecResults,
+                                                        const std::vector <std::string>& labels,
+                                                        double scale, double zeroPoint);
+    template bool AsrClassifier::GetTopResults<int8_t>(TfLiteTensor* tensor,
+                                                       std::vector<ClassificationResult>& vecResults,
+                                                       const std::vector <std::string>& labels,
+                                                       double scale, double zeroPoint);
+
+    bool AsrClassifier::GetClassificationResults(
+            TfLiteTensor* outputTensor,
+            std::vector<ClassificationResult>& vecResults,
+            const std::vector <std::string>& labels, uint32_t topNCount, bool use_softmax)
+    {
+            UNUSED(use_softmax);
+            vecResults.clear();
+
+            constexpr int minTensorDims = static_cast<int>(
+                (Wav2LetterModel::ms_outputRowsIdx > Wav2LetterModel::ms_outputColsIdx)?
+                 Wav2LetterModel::ms_outputRowsIdx : Wav2LetterModel::ms_outputColsIdx);
+
+            constexpr uint32_t outColsIdx = Wav2LetterModel::ms_outputColsIdx;
+
+            /* Sanity checks. */
+            if (outputTensor == nullptr) {
+                printf_err("Output vector is null pointer.\n");
+                return false;
+            } else if (outputTensor->dims->size < minTensorDims) {
+                printf_err("Output tensor expected to be %dD\n", minTensorDims);
+                return false;
+            } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) < topNCount) {
+                printf_err("Output vectors are smaller than %" PRIu32 "\n", topNCount);
+                return false;
+            } else if (static_cast<uint32_t>(outputTensor->dims->data[outColsIdx]) != labels.size()) {
+                printf("Output size doesn't match the labels' size\n");
+                return false;
+            }
+
+            if (topNCount != 1) {
+                warn("TopNCount value ignored in this implementation\n");
+            }
+
+            /* To return the floating point values, we need quantization parameters. */
+            QuantParams quantParams = GetTensorQuantParams(outputTensor);
+
+            bool resultState;
+
+            switch (outputTensor->type) {
+                case kTfLiteUInt8:
+                    resultState = this->GetTopResults<uint8_t>(
+                            outputTensor, vecResults,
+                            labels, quantParams.scale,
+                            quantParams.offset);
+                    break;
+                case kTfLiteInt8:
+                    resultState = this->GetTopResults<int8_t>(
+                            outputTensor, vecResults,
+                            labels, quantParams.scale,
+                            quantParams.offset);
+                    break;
+                default:
+                    printf_err("Tensor type %s not supported by classifier\n",
+                        TfLiteTypeGetName(outputTensor->type));
+                    return false;
+            }
+
+            if (!resultState) {
+                printf_err("Failed to get sorted set\n");
+                return false;
+            }
+
+            return true;
+    }
+
+} /* namespace app */
+} /* namespace arm */
\ No newline at end of file
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 */
diff --git a/source/use_case/asr/src/Wav2LetterPostprocess.cc b/source/use_case/asr/src/Wav2LetterPostprocess.cc
index e3e1999..42f434e 100644
--- a/source/use_case/asr/src/Wav2LetterPostprocess.cc
+++ b/source/use_case/asr/src/Wav2LetterPostprocess.cc
@@ -24,7 +24,7 @@
 namespace arm {
 namespace app {
 
-    ASRPostProcess::ASRPostProcess(AsrClassifier& classifier, TfLiteTensor* outputTensor,
+    AsrPostProcess::AsrPostProcess(TfLiteTensor* outputTensor, AsrClassifier& classifier,
             const std::vector<std::string>& labels, std::vector<ClassificationResult>& results,
             const uint32_t outputContextLen,
             const uint32_t blankTokenIdx, const uint32_t reductionAxisIdx
@@ -38,11 +38,11 @@
             m_blankTokenIdx(blankTokenIdx),
             m_reductionAxisIdx(reductionAxisIdx)
     {
-        this->m_outputInnerLen = ASRPostProcess::GetOutputInnerLen(this->m_outputTensor, this->m_outputContextLen);
+        this->m_outputInnerLen = AsrPostProcess::GetOutputInnerLen(this->m_outputTensor, this->m_outputContextLen);
         this->m_totalLen = (2 * this->m_outputContextLen + this->m_outputInnerLen);
     }
 
-    bool ASRPostProcess::DoPostProcess()
+    bool AsrPostProcess::DoPostProcess()
     {
         /* Basic checks. */
         if (!this->IsInputValid(this->m_outputTensor, this->m_reductionAxisIdx)) {
@@ -51,7 +51,7 @@
 
         /* Irrespective of tensor type, we use unsigned "byte" */
         auto* ptrData = tflite::GetTensorData<uint8_t>(this->m_outputTensor);
-        const uint32_t elemSz = ASRPostProcess::GetTensorElementSize(this->m_outputTensor);
+        const uint32_t elemSz = AsrPostProcess::GetTensorElementSize(this->m_outputTensor);
 
         /* Other sanity checks. */
         if (0 == elemSz) {
@@ -79,7 +79,7 @@
         return true;
     }
 
-    bool ASRPostProcess::IsInputValid(TfLiteTensor* tensor, const uint32_t axisIdx) const
+    bool AsrPostProcess::IsInputValid(TfLiteTensor* tensor, const uint32_t axisIdx) const
     {
         if (nullptr == tensor) {
             return false;
@@ -101,7 +101,7 @@
         return true;
     }
 
-    uint32_t ASRPostProcess::GetTensorElementSize(TfLiteTensor* tensor)
+    uint32_t AsrPostProcess::GetTensorElementSize(TfLiteTensor* tensor)
     {
         switch(tensor->type) {
             case kTfLiteUInt8:
@@ -120,7 +120,7 @@
         return 0;
     }
 
-    bool ASRPostProcess::EraseSectionsRowWise(
+    bool AsrPostProcess::EraseSectionsRowWise(
             uint8_t*         ptrData,
             const uint32_t   strideSzBytes,
             const bool       lastIteration)
@@ -157,7 +157,7 @@
         return true;
     }
 
-    uint32_t ASRPostProcess::GetNumFeatureVectors(const Model& model)
+    uint32_t AsrPostProcess::GetNumFeatureVectors(const Model& model)
     {
         TfLiteTensor* inputTensor = model.GetInputTensor(0);
         const int inputRows = std::max(inputTensor->dims->data[Wav2LetterModel::ms_inputRowsIdx], 0);
@@ -168,21 +168,23 @@
         return inputRows;
     }
 
-    uint32_t ASRPostProcess::GetOutputInnerLen(const TfLiteTensor* outputTensor, const uint32_t outputCtxLen)
+    uint32_t AsrPostProcess::GetOutputInnerLen(const TfLiteTensor* outputTensor, const uint32_t outputCtxLen)
     {
         const uint32_t outputRows = std::max(outputTensor->dims->data[Wav2LetterModel::ms_outputRowsIdx], 0);
         if (outputRows == 0) {
             printf_err("Error getting number of output rows for axis: %" PRIu32 "\n",
                     Wav2LetterModel::ms_outputRowsIdx);
         }
+
+        /* Watching for underflow. */
         int innerLen = (outputRows - (2 * outputCtxLen));
 
         return std::max(innerLen, 0);
     }
 
-    uint32_t ASRPostProcess::GetOutputContextLen(const Model& model, const uint32_t inputCtxLen)
+    uint32_t AsrPostProcess::GetOutputContextLen(const Model& model, const uint32_t inputCtxLen)
     {
-        const uint32_t inputRows = ASRPostProcess::GetNumFeatureVectors(model);
+        const uint32_t inputRows = AsrPostProcess::GetNumFeatureVectors(model);
         const uint32_t inputInnerLen = inputRows - (2 * inputCtxLen);
         constexpr uint32_t ms_outputRowsIdx = Wav2LetterModel::ms_outputRowsIdx;
 
diff --git a/source/use_case/asr/src/Wav2LetterPreprocess.cc b/source/use_case/asr/src/Wav2LetterPreprocess.cc
index 590d08a..92b0631 100644
--- a/source/use_case/asr/src/Wav2LetterPreprocess.cc
+++ b/source/use_case/asr/src/Wav2LetterPreprocess.cc
@@ -25,9 +25,9 @@
 namespace arm {
 namespace app {
 
-    ASRPreProcess::ASRPreProcess(TfLiteTensor* inputTensor, const uint32_t numMfccFeatures,
-            const uint32_t numFeatureFrames, const uint32_t mfccWindowLen,
-            const uint32_t mfccWindowStride
+    AsrPreProcess::AsrPreProcess(TfLiteTensor* inputTensor, const uint32_t numMfccFeatures,
+                                 const uint32_t numFeatureFrames, const uint32_t mfccWindowLen,
+                                 const uint32_t mfccWindowStride
             ):
             m_mfcc(numMfccFeatures, mfccWindowLen),
             m_inputTensor(inputTensor),
@@ -44,7 +44,7 @@
         }
     }
 
-    bool ASRPreProcess::DoPreProcess(const void* audioData, const size_t audioDataLen)
+    bool AsrPreProcess::DoPreProcess(const void* audioData, const size_t audioDataLen)
     {
         this->m_mfccSlidingWindow = audio::SlidingWindow<const int16_t>(
                 static_cast<const int16_t*>(audioData), audioDataLen,
@@ -82,7 +82,7 @@
         }
 
         /* Compute first and second order deltas from MFCCs. */
-        ASRPreProcess::ComputeDeltas(this->m_mfccBuf, this->m_delta1Buf, this->m_delta2Buf);
+        AsrPreProcess::ComputeDeltas(this->m_mfccBuf, this->m_delta1Buf, this->m_delta2Buf);
 
         /* Standardize calculated features. */
         this->Standarize();
@@ -112,9 +112,9 @@
         return false;
     }
 
-    bool ASRPreProcess::ComputeDeltas(Array2d<float>& mfcc,
-                                   Array2d<float>& delta1,
-                                   Array2d<float>& delta2)
+    bool AsrPreProcess::ComputeDeltas(Array2d<float>& mfcc,
+                                      Array2d<float>& delta1,
+                                      Array2d<float>& delta2)
     {
         const std::vector <float> delta1Coeffs =
             {6.66666667e-02,  5.00000000e-02,  3.33333333e-02,
@@ -167,7 +167,7 @@
         return true;
     }
 
-    void ASRPreProcess::StandardizeVecF32(Array2d<float>& vec)
+    void AsrPreProcess::StandardizeVecF32(Array2d<float>& vec)
     {
         auto mean = math::MathUtils::MeanF32(vec.begin(), vec.totalSize());
         auto stddev = math::MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean);
@@ -186,14 +186,14 @@
         }
     }
 
-    void ASRPreProcess::Standarize()
+    void AsrPreProcess::Standarize()
     {
-        ASRPreProcess::StandardizeVecF32(this->m_mfccBuf);
-        ASRPreProcess::StandardizeVecF32(this->m_delta1Buf);
-        ASRPreProcess::StandardizeVecF32(this->m_delta2Buf);
+        AsrPreProcess::StandardizeVecF32(this->m_mfccBuf);
+        AsrPreProcess::StandardizeVecF32(this->m_delta1Buf);
+        AsrPreProcess::StandardizeVecF32(this->m_delta2Buf);
     }
 
-    float ASRPreProcess::GetQuantElem(
+    float AsrPreProcess::GetQuantElem(
                 const float     elem,
                 const float     quantScale,
                 const int       quantOffset,