diff --git a/source/use_case/asr/src/MainLoop.cc b/source/use_case/asr/src/MainLoop.cc
index 51b0b18..a1a9540 100644
--- a/source/use_case/asr/src/MainLoop.cc
+++ b/source/use_case/asr/src/MainLoop.cc
@@ -14,15 +14,12 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "hal.h"                     /* Brings in platform definitions. */
 #include "Labels.hpp"                /* For label strings. */
 #include "UseCaseHandler.hpp"        /* Handlers for different user options. */
 #include "Wav2LetterModel.hpp"       /* Model class for running inference. */
 #include "UseCaseCommonUtils.hpp"    /* Utils functions. */
 #include "AsrClassifier.hpp"         /* Classifier. */
 #include "InputFiles.hpp"            /* Generated audio clip header. */
-#include "Wav2LetterPreprocess.hpp"  /* Pre-processing class. */
-#include "Wav2LetterPostprocess.hpp" /* Post-processing class. */
 #include "log_macros.h"
 
 enum opcodes
@@ -48,23 +45,9 @@
     fflush(stdout);
 }
 
-/** @brief Verify input and output tensor are of certain min dimensions. */
+/** @brief   Verify input and output tensor are of certain min dimensions. */
 static bool VerifyTensorDimensions(const arm::app::Model& model);
 
-/** @brief Gets the number of MFCC features for a single window. */
-static uint32_t GetNumMfccFeatures(const arm::app::Model& model);
-
-/** @brief Gets the number of MFCC feature vectors to be computed. */
-static uint32_t GetNumMfccFeatureVectors(const arm::app::Model& model);
-
-/** @brief Gets the output context length (left and right) for post-processing. */
-static uint32_t GetOutputContextLen(const arm::app::Model& model,
-                                    uint32_t inputCtxLen);
-
-/** @brief Gets the output inner length for post-processing. */
-static uint32_t GetOutputInnerLen(const arm::app::Model& model,
-                                  uint32_t outputCtxLen);
-
 void main_loop()
 {
     arm::app::Wav2LetterModel model;  /* Model wrapper object. */
@@ -78,21 +61,6 @@
         return;
     }
 
-    /* Initialise pre-processing. */
-    arm::app::audio::asr::Preprocess prep(
-                                GetNumMfccFeatures(model),
-                                g_FrameLength,
-                                g_FrameStride,
-                                GetNumMfccFeatureVectors(model));
-
-    /* Initialise post-processing. */
-    const uint32_t outputCtxLen = GetOutputContextLen(model, g_ctxLen);
-    const uint32_t blankTokenIdx = 28;
-    arm::app::audio::asr::Postprocess postp(
-                                outputCtxLen,
-                                GetOutputInnerLen(model, outputCtxLen),
-                                blankTokenIdx);
-
     /* Instantiate application context. */
     arm::app::ApplicationContext caseContext;
     std::vector <std::string> labels;
@@ -109,8 +77,6 @@
     caseContext.Set<uint32_t>("ctxLen", g_ctxLen);  /* Left and right context length (MFCC feat vectors). */
     caseContext.Set<const std::vector <std::string>&>("labels", labels);
     caseContext.Set<arm::app::AsrClassifier&>("classifier", classifier);
-    caseContext.Set<arm::app::audio::asr::Preprocess&>("preprocess", prep);
-    caseContext.Set<arm::app::audio::asr::Postprocess&>("postprocess", postp);
 
     bool executionSuccessful = true;
     constexpr bool bUseMenu = NUMBER_OF_FILES > 1 ? true : false;
@@ -184,52 +150,3 @@
 
     return true;
 }
-
-static uint32_t GetNumMfccFeatures(const arm::app::Model& model)
-{
-    TfLiteTensor* inputTensor = model.GetInputTensor(0);
-    const int inputCols = inputTensor->dims->data[arm::app::Wav2LetterModel::ms_inputColsIdx];
-    if (0 != inputCols % 3) {
-        printf_err("Number of input columns is not a multiple of 3\n");
-    }
-    return std::max(inputCols/3, 0);
-}
-
-static uint32_t GetNumMfccFeatureVectors(const arm::app::Model& model)
-{
-    TfLiteTensor* inputTensor = model.GetInputTensor(0);
-    const int inputRows = inputTensor->dims->data[arm::app::Wav2LetterModel::ms_inputRowsIdx];
-    return std::max(inputRows, 0);
-}
-
-static uint32_t GetOutputContextLen(const arm::app::Model& model, const uint32_t inputCtxLen)
-{
-    const uint32_t inputRows = GetNumMfccFeatureVectors(model);
-    const uint32_t inputInnerLen = inputRows - (2 * inputCtxLen);
-    constexpr uint32_t ms_outputRowsIdx = arm::app::Wav2LetterModel::ms_outputRowsIdx;
-
-    /* Check to make sure that the input tensor supports the above
-     * context and inner lengths. */
-    if (inputRows <= 2 * inputCtxLen || inputRows <= inputInnerLen) {
-        printf_err("Input rows not compatible with ctx of %" PRIu32 "\n",
-            inputCtxLen);
-        return 0;
-    }
-
-    TfLiteTensor* outputTensor = model.GetOutputTensor(0);
-    const uint32_t outputRows = std::max(outputTensor->dims->data[ms_outputRowsIdx], 0);
-
-    const float tensorColRatio = static_cast<float>(inputRows)/
-                                     static_cast<float>(outputRows);
-
-    return std::round(static_cast<float>(inputCtxLen)/tensorColRatio);
-}
-
-static uint32_t GetOutputInnerLen(const arm::app::Model& model,
-                                  const uint32_t outputCtxLen)
-{
-    constexpr uint32_t ms_outputRowsIdx = arm::app::Wav2LetterModel::ms_outputRowsIdx;
-    TfLiteTensor* outputTensor = model.GetOutputTensor(0);
-    const uint32_t outputRows = std::max(outputTensor->dims->data[ms_outputRowsIdx], 0);
-    return (outputRows - (2 * outputCtxLen));
-}
diff --git a/source/use_case/asr/src/UseCaseHandler.cc b/source/use_case/asr/src/UseCaseHandler.cc
index 420f725..7fe959b 100644
--- a/source/use_case/asr/src/UseCaseHandler.cc
+++ b/source/use_case/asr/src/UseCaseHandler.cc
@@ -20,7 +20,6 @@
 #include "AsrClassifier.hpp"
 #include "Wav2LetterModel.hpp"
 #include "hal.h"
-#include "Wav2LetterMfcc.hpp"
 #include "AudioUtils.hpp"
 #include "ImageUtils.hpp"
 #include "UseCaseCommonUtils.hpp"
@@ -34,68 +33,63 @@
 namespace app {
 
     /**
-     * @brief           Presents inference results using the data presentation
-     *                  object.
-     * @param[in]       results     Vector of classification results to be displayed.
+     * @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<arm::app::asr::AsrResult>& results);
+    static bool PresentInferenceResult(const std::vector<asr::AsrResult>& results);
 
-    /* Audio inference classification handler. */
+    /* ASR inference handler. */
     bool ClassifyAudioHandler(ApplicationContext& ctx, uint32_t clipIndex, bool runAll)
     {
-        constexpr uint32_t dataPsnTxtInfStartX = 20;
-        constexpr uint32_t dataPsnTxtInfStartY = 40;
-
-        hal_lcd_clear(COLOR_BLACK);
-
+        auto& model = ctx.Get<Model&>("model");
         auto& profiler = ctx.Get<Profiler&>("profiler");
-
+        auto mfccFrameLen = ctx.Get<uint32_t>("frameLength");
+        auto mfccFrameStride = ctx.Get<uint32_t>("frameStride");
+        auto scoreThreshold = ctx.Get<float>("scoreThreshold");
+        auto inputCtxLen = ctx.Get<uint32_t>("ctxLen");
         /* If the request has a valid size, set the audio index. */
         if (clipIndex < NUMBER_OF_FILES) {
             if (!SetAppCtxIfmIdx(ctx, clipIndex,"clipIndex")) {
                 return false;
             }
         }
+        auto initialClipIdx = ctx.Get<uint32_t>("clipIndex");
+        constexpr uint32_t dataPsnTxtInfStartX = 20;
+        constexpr uint32_t dataPsnTxtInfStartY = 40;
 
-        /* Get model reference. */
-        auto& model = ctx.Get<Model&>("model");
         if (!model.IsInited()) {
             printf_err("Model is not initialised! Terminating processing.\n");
             return false;
         }
 
-        /* Get score threshold to be applied for the classifier (post-inference). */
-        auto scoreThreshold = ctx.Get<float>("scoreThreshold");
-
-        /* Get tensors. Dimensions of the tensor should have been verified by
+        /* Get input shape. Dimensions of the tensor should have been verified by
          * the callee. */
-        TfLiteTensor* inputTensor = model.GetInputTensor(0);
-        TfLiteTensor* outputTensor = model.GetOutputTensor(0);
-        const uint32_t inputRows = inputTensor->dims->data[arm::app::Wav2LetterModel::ms_inputRowsIdx];
+        TfLiteIntArray* inputShape = model.GetInputShape(0);
 
-        /* Populate MFCC related parameters. */
-        auto mfccParamsWinLen = ctx.Get<uint32_t>("frameLength");
-        auto mfccParamsWinStride = ctx.Get<uint32_t>("frameStride");
-
-        /* Populate ASR inference context and inner lengths for input. */
-        auto inputCtxLen = ctx.Get<uint32_t>("ctxLen");
-        const uint32_t inputInnerLen = inputRows - (2 * inputCtxLen);
+        const uint32_t inputRowsSize = inputShape->data[Wav2LetterModel::ms_inputRowsIdx];
+        const uint32_t inputInnerLen = inputRowsSize - (2 * inputCtxLen);
 
         /* Audio data stride corresponds to inputInnerLen feature vectors. */
-        const uint32_t audioParamsWinLen = (inputRows - 1) * mfccParamsWinStride + (mfccParamsWinLen);
-        const uint32_t audioParamsWinStride = inputInnerLen * mfccParamsWinStride;
-        const float audioParamsSecondsPerSample = (1.0/audio::Wav2LetterMFCC::ms_defaultSamplingFreq);
+        const uint32_t audioDataWindowLen = (inputRowsSize - 1) * mfccFrameStride + (mfccFrameLen);
+        const uint32_t audioDataWindowStride = inputInnerLen * mfccFrameStride;
 
-        /* Get pre/post-processing objects. */
-        auto& prep = ctx.Get<audio::asr::Preprocess&>("preprocess");
-        auto& postp = ctx.Get<audio::asr::Postprocess&>("postprocess");
+        /* NOTE: This is only used for time stamp calculation. */
+        const float secondsPerSample = (1.0 / audio::Wav2LetterMFCC::ms_defaultSamplingFreq);
 
-        /* Set default reduction axis for post-processing. */
-        const uint32_t reductionAxis = arm::app::Wav2LetterModel::ms_outputRowsIdx;
+        /* Set up pre and post-processing objects. */
+        ASRPreProcess preProcess = ASRPreProcess(model.GetInputTensor(0), Wav2LetterModel::ms_numMfccFeatures,
+                inputShape->data[Wav2LetterModel::ms_inputRowsIdx], mfccFrameLen, mfccFrameStride);
 
-        /* Audio clip start index. */
-        auto startClipIdx = ctx.Get<uint32_t>("clipIndex");
+        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"),
+                singleInfResult, outputCtxLen,
+                Wav2LetterModel::ms_blankTokenIdx, Wav2LetterModel::ms_outputRowsIdx
+                );
+
+        UseCaseRunner runner = UseCaseRunner(&preProcess, &postProcess, &model);
 
         /* Loop to process audio clips. */
         do {
@@ -109,44 +103,41 @@
             const uint32_t audioArrSize = get_audio_array_size(currentIndex);
 
             if (!audioArr) {
-                printf_err("Invalid audio array pointer\n");
+                printf_err("Invalid audio array pointer.\n");
                 return false;
             }
 
-            /* Audio clip must have enough samples to produce 1 MFCC feature. */
-            if (audioArrSize < mfccParamsWinLen) {
+            /* Audio clip needs enough samples to produce at least 1 MFCC feature. */
+            if (audioArrSize < mfccFrameLen) {
                 printf_err("Not enough audio samples, minimum needed is %" PRIu32 "\n",
-                    mfccParamsWinLen);
+                           mfccFrameLen);
                 return false;
             }
 
-            /* Initialise an audio slider. */
+            /* Creating a sliding window through the whole audio clip. */
             auto audioDataSlider = audio::FractionalSlidingWindow<const int16_t>(
-                                        audioArr,
-                                        audioArrSize,
-                                        audioParamsWinLen,
-                                        audioParamsWinStride);
+                    audioArr, audioArrSize,
+                    audioDataWindowLen, audioDataWindowStride);
 
-            /* Declare a container for results. */
-            std::vector<arm::app::asr::AsrResult> results;
+            /* Declare a container for final results. */
+            std::vector<asr::AsrResult> finalResults;
 
             /* Display message on the LCD - inference running. */
             std::string str_inf{"Running inference... "};
-            hal_lcd_display_text(
-                                str_inf.c_str(), str_inf.size(),
-                                dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+            hal_lcd_display_text(str_inf.c_str(), str_inf.size(),
+                    dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
 
             info("Running inference on audio clip %" PRIu32 " => %s\n", currentIndex,
                  get_filename(currentIndex));
 
-            size_t inferenceWindowLen = audioParamsWinLen;
+            size_t inferenceWindowLen = audioDataWindowLen;
 
             /* Start sliding through audio clip. */
             while (audioDataSlider.HasNext()) {
 
-                /* If not enough audio see how much can be sent for processing. */
+                /* If not enough audio, see how much can be sent for processing. */
                 size_t nextStartIndex = audioDataSlider.NextWindowStartIndex();
-                if (nextStartIndex + audioParamsWinLen > audioArrSize) {
+                if (nextStartIndex + audioDataWindowLen > audioArrSize) {
                     inferenceWindowLen = audioArrSize - nextStartIndex;
                 }
 
@@ -155,46 +146,40 @@
                 info("Inference %zu/%zu\n", audioDataSlider.Index() + 1,
                      static_cast<size_t>(ceilf(audioDataSlider.FractionalTotalStrides() + 1)));
 
-                /* Calculate MFCCs, deltas and populate the input tensor. */
-                prep.Invoke(inferenceWindow, inferenceWindowLen, inputTensor);
+                /* Run the pre-processing, inference and post-processing. */
+                runner.PreProcess(inferenceWindow, inferenceWindowLen);
 
-                /* Run inference over this audio clip sliding window. */
-                if (!RunInference(model, profiler)) {
+                profiler.StartProfiling("Inference");
+                if (!runner.RunInference()) {
+                    return false;
+                }
+                profiler.StopProfiling();
+
+                postProcess.m_lastIteration = !audioDataSlider.HasNext();
+                if (!runner.PostProcess()) {
                     return false;
                 }
 
-                /* Post-process. */
-                postp.Invoke(outputTensor, reductionAxis, !audioDataSlider.HasNext());
-
-                /* Get results. */
-                std::vector<ClassificationResult> classificationResult;
-                auto& classifier = ctx.Get<AsrClassifier&>("classifier");
-                classifier.GetClassificationResults(
-                            outputTensor, classificationResult,
-                            ctx.Get<std::vector<std::string>&>("labels"), 1);
-
-                results.emplace_back(asr::AsrResult(classificationResult,
-                                                    (audioDataSlider.Index() *
-                                                    audioParamsSecondsPerSample *
-                                                    audioParamsWinStride),
-                                                    audioDataSlider.Index(), scoreThreshold));
+                /* Add results from this window to our final results vector. */
+                finalResults.emplace_back(asr::AsrResult(singleInfResult,
+                        (audioDataSlider.Index() * secondsPerSample * audioDataWindowStride),
+                        audioDataSlider.Index(), scoreThreshold));
 
 #if VERIFY_TEST_OUTPUT
-                arm::app::DumpTensor(outputTensor,
-                    outputTensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx]);
+                TfLiteTensor* outputTensor = model.GetOutputTensor(0);
+                armDumpTensor(outputTensor,
+                    outputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx]);
 #endif /* VERIFY_TEST_OUTPUT */
-
-            }
+            } /* while (audioDataSlider.HasNext()) */
 
             /* Erase. */
             str_inf = std::string(str_inf.size(), ' ');
-            hal_lcd_display_text(
-                                str_inf.c_str(), str_inf.size(),
-                                dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+            hal_lcd_display_text(str_inf.c_str(), str_inf.size(),
+                    dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
 
-            ctx.Set<std::vector<arm::app::asr::AsrResult>>("results", results);
+            ctx.Set<std::vector<asr::AsrResult>>("results", finalResults);
 
-            if (!PresentInferenceResult(results)) {
+            if (!PresentInferenceResult(finalResults)) {
                 return false;
             }
 
@@ -202,13 +187,13 @@
 
             IncrementAppCtxIfmIdx(ctx,"clipIndex");
 
-        } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
+        } while (runAll && ctx.Get<uint32_t>("clipIndex") != initialClipIdx);
 
         return true;
     }
 
 
-    static bool PresentInferenceResult(const std::vector<arm::app::asr::AsrResult>& results)
+    static bool PresentInferenceResult(const std::vector<asr::AsrResult>& results)
     {
         constexpr uint32_t dataPsnTxtStartX1 = 20;
         constexpr uint32_t dataPsnTxtStartY1 = 60;
@@ -219,15 +204,15 @@
         info("Final results:\n");
         info("Total number of inferences: %zu\n", results.size());
         /* Results from multiple inferences should be combined before processing. */
-        std::vector<arm::app::ClassificationResult> combinedResults;
-        for (auto& result : results) {
+        std::vector<ClassificationResult> combinedResults;
+        for (const auto& result : results) {
             combinedResults.insert(combinedResults.end(),
                                    result.m_resultVec.begin(),
                                    result.m_resultVec.end());
         }
 
         /* Get each inference result string using the decoder. */
-        for (const auto & result : results) {
+        for (const auto& result : results) {
             std::string infResultStr = audio::asr::DecodeOutput(result.m_resultVec);
 
             info("For timestamp: %f (inference #: %" PRIu32 "); label: %s\n",
@@ -238,10 +223,9 @@
         /* Get the decoded result for the combined result. */
         std::string finalResultStr = audio::asr::DecodeOutput(combinedResults);
 
-        hal_lcd_display_text(
-                            finalResultStr.c_str(), finalResultStr.size(),
-                            dataPsnTxtStartX1, dataPsnTxtStartY1,
-                            allow_multiple_lines);
+        hal_lcd_display_text(finalResultStr.c_str(), finalResultStr.size(),
+                dataPsnTxtStartX1, dataPsnTxtStartY1,
+                allow_multiple_lines);
 
         info("Complete recognition: %s\n", finalResultStr.c_str());
         return true;
diff --git a/source/use_case/asr/src/Wav2LetterMfcc.cc b/source/use_case/asr/src/Wav2LetterMfcc.cc
index 1bcaa66..bb29b0f 100644
--- a/source/use_case/asr/src/Wav2LetterMfcc.cc
+++ b/source/use_case/asr/src/Wav2LetterMfcc.cc
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 #include "Wav2LetterMfcc.hpp"
+
 #include "PlatformMath.hpp"
 #include "log_macros.h"
 
diff --git a/source/use_case/asr/src/Wav2LetterModel.cc b/source/use_case/asr/src/Wav2LetterModel.cc
index 766bce9..8b38f4f 100644
--- a/source/use_case/asr/src/Wav2LetterModel.cc
+++ b/source/use_case/asr/src/Wav2LetterModel.cc
@@ -15,6 +15,7 @@
  * limitations under the License.
  */
 #include "Wav2LetterModel.hpp"
+
 #include "log_macros.h"
 
 
diff --git a/source/use_case/asr/src/Wav2LetterPostprocess.cc b/source/use_case/asr/src/Wav2LetterPostprocess.cc
index 0392061..e3e1999 100644
--- a/source/use_case/asr/src/Wav2LetterPostprocess.cc
+++ b/source/use_case/asr/src/Wav2LetterPostprocess.cc
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
+ * Copyright (c) 2021-2022 Arm Limited. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -15,67 +15,71 @@
  * limitations under the License.
  */
 #include "Wav2LetterPostprocess.hpp"
+
 #include "Wav2LetterModel.hpp"
 #include "log_macros.h"
 
+#include <cmath>
+
 namespace arm {
 namespace app {
-namespace audio {
-namespace asr {
 
-    Postprocess::Postprocess(const uint32_t contextLen,
-                             const uint32_t innerLen,
-                             const uint32_t blankTokenIdx)
-        :   m_contextLen(contextLen),
-            m_innerLen(innerLen),
-            m_totalLen(2 * this->m_contextLen + this->m_innerLen),
+    ASRPostProcess::ASRPostProcess(AsrClassifier& classifier, TfLiteTensor* outputTensor,
+            const std::vector<std::string>& labels, std::vector<ClassificationResult>& results,
+            const uint32_t outputContextLen,
+            const uint32_t blankTokenIdx, const uint32_t reductionAxisIdx
+            ):
+            m_classifier(classifier),
+            m_outputTensor(outputTensor),
+            m_labels{labels},
+            m_results(results),
+            m_outputContextLen(outputContextLen),
             m_countIterations(0),
-            m_blankTokenIdx(blankTokenIdx)
-    {}
+            m_blankTokenIdx(blankTokenIdx),
+            m_reductionAxisIdx(reductionAxisIdx)
+    {
+        this->m_outputInnerLen = ASRPostProcess::GetOutputInnerLen(this->m_outputTensor, this->m_outputContextLen);
+        this->m_totalLen = (2 * this->m_outputContextLen + this->m_outputInnerLen);
+    }
 
-    bool Postprocess::Invoke(TfLiteTensor*  tensor,
-                            const uint32_t  axisIdx,
-                            const bool      lastIteration)
+    bool ASRPostProcess::DoPostProcess()
     {
         /* Basic checks. */
-        if (!this->IsInputValid(tensor, axisIdx)) {
+        if (!this->IsInputValid(this->m_outputTensor, this->m_reductionAxisIdx)) {
             return false;
         }
 
         /* Irrespective of tensor type, we use unsigned "byte" */
-        uint8_t* ptrData = tflite::GetTensorData<uint8_t>(tensor);
-        const uint32_t elemSz = this->GetTensorElementSize(tensor);
+        auto* ptrData = tflite::GetTensorData<uint8_t>(this->m_outputTensor);
+        const uint32_t elemSz = ASRPostProcess::GetTensorElementSize(this->m_outputTensor);
 
         /* Other sanity checks. */
         if (0 == elemSz) {
             printf_err("Tensor type not supported for post processing\n");
             return false;
-        } else if (elemSz * this->m_totalLen > tensor->bytes) {
+        } else if (elemSz * this->m_totalLen > this->m_outputTensor->bytes) {
             printf_err("Insufficient number of tensor bytes\n");
             return false;
         }
 
         /* Which axis do we need to process? */
-        switch (axisIdx) {
-            case arm::app::Wav2LetterModel::ms_outputRowsIdx:
-                return this->EraseSectionsRowWise(ptrData,
-                                                  elemSz *
-                                                  tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx],
-                                                  lastIteration);
-            case arm::app::Wav2LetterModel::ms_outputColsIdx:
-                return this->EraseSectionsColWise(ptrData,
-                                                  elemSz *
-                                                  tensor->dims->data[arm::app::Wav2LetterModel::ms_outputRowsIdx],
-                                                  lastIteration);
+        switch (this->m_reductionAxisIdx) {
+            case Wav2LetterModel::ms_outputRowsIdx:
+                this->EraseSectionsRowWise(
+                        ptrData, elemSz * this->m_outputTensor->dims->data[Wav2LetterModel::ms_outputColsIdx],
+                        this->m_lastIteration);
+                break;
             default:
-                printf_err("Unsupported axis index: %" PRIu32 "\n", axisIdx);
+                printf_err("Unsupported axis index: %" PRIu32 "\n", this->m_reductionAxisIdx);
+                return false;
         }
+        this->m_classifier.GetClassificationResults(this->m_outputTensor,
+                this->m_results, this->m_labels, 1);
 
-        return false;
+        return true;
     }
 
-    bool Postprocess::IsInputValid(TfLiteTensor*  tensor,
-                                   const uint32_t axisIdx) const
+    bool ASRPostProcess::IsInputValid(TfLiteTensor* tensor, const uint32_t axisIdx) const
     {
         if (nullptr == tensor) {
             return false;
@@ -89,15 +93,15 @@
 
         if (static_cast<int>(this->m_totalLen) !=
                              tensor->dims->data[axisIdx]) {
-            printf_err("Unexpected tensor dimension for axis %d, \n",
-                tensor->dims->data[axisIdx]);
+            printf_err("Unexpected tensor dimension for axis %d, got %d, \n",
+                axisIdx, tensor->dims->data[axisIdx]);
             return false;
         }
 
         return true;
     }
 
-    uint32_t Postprocess::GetTensorElementSize(TfLiteTensor*  tensor)
+    uint32_t ASRPostProcess::GetTensorElementSize(TfLiteTensor* tensor)
     {
         switch(tensor->type) {
             case kTfLiteUInt8:
@@ -116,30 +120,30 @@
         return 0;
     }
 
-    bool Postprocess::EraseSectionsRowWise(
-                        uint8_t*         ptrData,
-                        const uint32_t   strideSzBytes,
-                        const bool       lastIteration)
+    bool ASRPostProcess::EraseSectionsRowWise(
+            uint8_t*         ptrData,
+            const uint32_t   strideSzBytes,
+            const bool       lastIteration)
     {
         /* In this case, the "zero-ing" is quite simple as the region
          * to be zeroed sits in contiguous memory (row-major). */
-        const uint32_t eraseLen = strideSzBytes * this->m_contextLen;
+        const uint32_t eraseLen = strideSzBytes * this->m_outputContextLen;
 
         /* Erase left context? */
         if (this->m_countIterations > 0) {
             /* Set output of each classification window to the blank token. */
             std::memset(ptrData, 0, eraseLen);
-            for (size_t windowIdx = 0; windowIdx < this->m_contextLen; windowIdx++) {
+            for (size_t windowIdx = 0; windowIdx < this->m_outputContextLen; windowIdx++) {
                 ptrData[windowIdx*strideSzBytes + this->m_blankTokenIdx] = 1;
             }
         }
 
         /* Erase right context? */
         if (false == lastIteration) {
-            uint8_t * rightCtxPtr = ptrData + (strideSzBytes * (this->m_contextLen + this->m_innerLen));
+            uint8_t* rightCtxPtr = ptrData + (strideSzBytes * (this->m_outputContextLen + this->m_outputInnerLen));
             /* Set output of each classification window to the blank token. */
             std::memset(rightCtxPtr, 0, eraseLen);
-            for (size_t windowIdx = 0; windowIdx < this->m_contextLen; windowIdx++) {
+            for (size_t windowIdx = 0; windowIdx < this->m_outputContextLen; windowIdx++) {
                 rightCtxPtr[windowIdx*strideSzBytes + this->m_blankTokenIdx] = 1;
             }
         }
@@ -153,19 +157,56 @@
         return true;
     }
 
-    bool Postprocess::EraseSectionsColWise(
-                        const uint8_t*         ptrData,
-                        const uint32_t   strideSzBytes,
-                        const bool       lastIteration)
+    uint32_t ASRPostProcess::GetNumFeatureVectors(const Model& model)
     {
-        /* Not implemented. */
-        UNUSED(ptrData);
-        UNUSED(strideSzBytes);
-        UNUSED(lastIteration);
-        return false;
+        TfLiteTensor* inputTensor = model.GetInputTensor(0);
+        const int inputRows = std::max(inputTensor->dims->data[Wav2LetterModel::ms_inputRowsIdx], 0);
+        if (inputRows == 0) {
+            printf_err("Error getting number of input rows for axis: %" PRIu32 "\n",
+                    Wav2LetterModel::ms_inputRowsIdx);
+        }
+        return inputRows;
     }
 
-} /* namespace asr */
-} /* namespace audio */
+    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);
+        }
+        int innerLen = (outputRows - (2 * outputCtxLen));
+
+        return std::max(innerLen, 0);
+    }
+
+    uint32_t ASRPostProcess::GetOutputContextLen(const Model& model, const uint32_t inputCtxLen)
+    {
+        const uint32_t inputRows = ASRPostProcess::GetNumFeatureVectors(model);
+        const uint32_t inputInnerLen = inputRows - (2 * inputCtxLen);
+        constexpr uint32_t ms_outputRowsIdx = Wav2LetterModel::ms_outputRowsIdx;
+
+        /* Check to make sure that the input tensor supports the above
+         * context and inner lengths. */
+        if (inputRows <= 2 * inputCtxLen || inputRows <= inputInnerLen) {
+            printf_err("Input rows not compatible with ctx of %" PRIu32 "\n",
+                       inputCtxLen);
+            return 0;
+        }
+
+        TfLiteTensor* outputTensor = model.GetOutputTensor(0);
+        const uint32_t outputRows = std::max(outputTensor->dims->data[ms_outputRowsIdx], 0);
+        if (outputRows == 0) {
+            printf_err("Error getting number of output rows for axis: %" PRIu32 "\n",
+                       Wav2LetterModel::ms_outputRowsIdx);
+            return 0;
+        }
+
+        const float inOutRowRatio = static_cast<float>(inputRows) /
+                                     static_cast<float>(outputRows);
+
+        return std::round(static_cast<float>(inputCtxLen) / inOutRowRatio);
+    }
+
 } /* namespace app */
 } /* namespace arm */
\ No newline at end of file
diff --git a/source/use_case/asr/src/Wav2LetterPreprocess.cc b/source/use_case/asr/src/Wav2LetterPreprocess.cc
index e5ac3ca..590d08a 100644
--- a/source/use_case/asr/src/Wav2LetterPreprocess.cc
+++ b/source/use_case/asr/src/Wav2LetterPreprocess.cc
@@ -1,5 +1,5 @@
 /*
- * Copyright (c) 2021 Arm Limited. All rights reserved.
+ * Copyright (c) 2021-2022 Arm Limited. All rights reserved.
  * SPDX-License-Identifier: Apache-2.0
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
@@ -24,37 +24,31 @@
 
 namespace arm {
 namespace app {
-namespace audio {
-namespace asr {
 
-    Preprocess::Preprocess(
-        const uint32_t  numMfccFeatures,
-        const uint32_t  windowLen,
-        const uint32_t  windowStride,
-        const uint32_t  numMfccVectors):
-            m_mfcc(numMfccFeatures, windowLen),
-            m_mfccBuf(numMfccFeatures, numMfccVectors),
-            m_delta1Buf(numMfccFeatures, numMfccVectors),
-            m_delta2Buf(numMfccFeatures, numMfccVectors),
-            m_windowLen(windowLen),
-            m_windowStride(windowStride),
+    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),
+            m_mfccBuf(numMfccFeatures, numFeatureFrames),
+            m_delta1Buf(numMfccFeatures, numFeatureFrames),
+            m_delta2Buf(numMfccFeatures, numFeatureFrames),
+            m_mfccWindowLen(mfccWindowLen),
+            m_mfccWindowStride(mfccWindowStride),
             m_numMfccFeats(numMfccFeatures),
-            m_numFeatVectors(numMfccVectors),
-            m_window()
+            m_numFeatureFrames(numFeatureFrames)
     {
-        if (numMfccFeatures > 0 && windowLen > 0) {
+        if (numMfccFeatures > 0 && mfccWindowLen > 0) {
             this->m_mfcc.Init();
         }
     }
 
-    bool Preprocess::Invoke(
-                const int16_t*  audioData,
-                const uint32_t  audioDataLen,
-                TfLiteTensor*   tensor)
+    bool ASRPreProcess::DoPreProcess(const void* audioData, const size_t audioDataLen)
     {
-        this->m_window = SlidingWindow<const int16_t>(
-                            audioData, audioDataLen,
-                            this->m_windowLen, this->m_windowStride);
+        this->m_mfccSlidingWindow = audio::SlidingWindow<const int16_t>(
+                static_cast<const int16_t*>(audioData), audioDataLen,
+                this->m_mfccWindowLen, this->m_mfccWindowStride);
 
         uint32_t mfccBufIdx = 0;
 
@@ -62,12 +56,12 @@
         std::fill(m_delta1Buf.begin(), m_delta1Buf.end(), 0.f);
         std::fill(m_delta2Buf.begin(), m_delta2Buf.end(), 0.f);
 
-        /* While we can slide over the window. */
-        while (this->m_window.HasNext()) {
-            const int16_t*  mfccWindow = this->m_window.Next();
+        /* While we can slide over the audio. */
+        while (this->m_mfccSlidingWindow.HasNext()) {
+            const int16_t* mfccWindow = this->m_mfccSlidingWindow.Next();
             auto mfccAudioData = std::vector<int16_t>(
                                         mfccWindow,
-                                        mfccWindow + this->m_windowLen);
+                                        mfccWindow + this->m_mfccWindowLen);
             auto mfcc = this->m_mfcc.MfccCompute(mfccAudioData);
             for (size_t i = 0; i < this->m_mfccBuf.size(0); ++i) {
                 this->m_mfccBuf(i, mfccBufIdx) = mfcc[i];
@@ -76,11 +70,11 @@
         }
 
         /* Pad MFCC if needed by adding MFCC for zeros. */
-        if (mfccBufIdx != this->m_numFeatVectors) {
-            std::vector<int16_t> zerosWindow = std::vector<int16_t>(this->m_windowLen, 0);
+        if (mfccBufIdx != this->m_numFeatureFrames) {
+            std::vector<int16_t> zerosWindow = std::vector<int16_t>(this->m_mfccWindowLen, 0);
             std::vector<float> mfccZeros = this->m_mfcc.MfccCompute(zerosWindow);
 
-            while (mfccBufIdx != this->m_numFeatVectors) {
+            while (mfccBufIdx != this->m_numFeatureFrames) {
                 memcpy(&this->m_mfccBuf(0, mfccBufIdx),
                        mfccZeros.data(), sizeof(float) * m_numMfccFeats);
                 ++mfccBufIdx;
@@ -88,39 +82,37 @@
         }
 
         /* Compute first and second order deltas from MFCCs. */
-        Preprocess::ComputeDeltas(this->m_mfccBuf,
-                            this->m_delta1Buf,
-                            this->m_delta2Buf);
+        ASRPreProcess::ComputeDeltas(this->m_mfccBuf, this->m_delta1Buf, this->m_delta2Buf);
 
-        /* Normalise. */
-        this->Normalise();
+        /* Standardize calculated features. */
+        this->Standarize();
 
         /* Quantise. */
-        QuantParams quantParams = GetTensorQuantParams(tensor);
+        QuantParams quantParams = GetTensorQuantParams(this->m_inputTensor);
 
         if (0 == quantParams.scale) {
             printf_err("Quantisation scale can't be 0\n");
             return false;
         }
 
-        switch(tensor->type) {
+        switch(this->m_inputTensor->type) {
             case kTfLiteUInt8:
                 return this->Quantise<uint8_t>(
-                        tflite::GetTensorData<uint8_t>(tensor), tensor->bytes,
+                        tflite::GetTensorData<uint8_t>(this->m_inputTensor), this->m_inputTensor->bytes,
                         quantParams.scale, quantParams.offset);
             case kTfLiteInt8:
                 return this->Quantise<int8_t>(
-                        tflite::GetTensorData<int8_t>(tensor), tensor->bytes,
+                        tflite::GetTensorData<int8_t>(this->m_inputTensor), this->m_inputTensor->bytes,
                         quantParams.scale, quantParams.offset);
             default:
                 printf_err("Unsupported tensor type %s\n",
-                    TfLiteTypeGetName(tensor->type));
+                    TfLiteTypeGetName(this->m_inputTensor->type));
         }
 
         return false;
     }
 
-    bool Preprocess::ComputeDeltas(Array2d<float>& mfcc,
+    bool ASRPreProcess::ComputeDeltas(Array2d<float>& mfcc,
                                    Array2d<float>& delta1,
                                    Array2d<float>& delta2)
     {
@@ -175,20 +167,10 @@
         return true;
     }
 
-    float Preprocess::GetMean(Array2d<float>& vec)
+    void ASRPreProcess::StandardizeVecF32(Array2d<float>& vec)
     {
-        return math::MathUtils::MeanF32(vec.begin(), vec.totalSize());
-    }
-
-    float Preprocess::GetStdDev(Array2d<float>& vec, const float mean)
-    {
-        return math::MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean);
-    }
-
-    void Preprocess::NormaliseVec(Array2d<float>& vec)
-    {
-        auto mean = Preprocess::GetMean(vec);
-        auto stddev = Preprocess::GetStdDev(vec, mean);
+        auto mean = math::MathUtils::MeanF32(vec.begin(), vec.totalSize());
+        auto stddev = math::MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean);
 
         debug("Mean: %f, Stddev: %f\n", mean, stddev);
         if (stddev == 0) {
@@ -204,14 +186,14 @@
         }
     }
 
-    void Preprocess::Normalise()
+    void ASRPreProcess::Standarize()
     {
-        Preprocess::NormaliseVec(this->m_mfccBuf);
-        Preprocess::NormaliseVec(this->m_delta1Buf);
-        Preprocess::NormaliseVec(this->m_delta2Buf);
+        ASRPreProcess::StandardizeVecF32(this->m_mfccBuf);
+        ASRPreProcess::StandardizeVecF32(this->m_delta1Buf);
+        ASRPreProcess::StandardizeVecF32(this->m_delta2Buf);
     }
 
-    float Preprocess::GetQuantElem(
+    float ASRPreProcess::GetQuantElem(
                 const float     elem,
                 const float     quantScale,
                 const int       quantOffset,
@@ -222,7 +204,5 @@
         return std::min<float>(std::max<float>(val, minVal), maxVal);
     }
 
-} /* namespace asr */
-} /* namespace audio */
 } /* namespace app */
 } /* namespace arm */
\ No newline at end of file