diff --git a/source/use_case/kws_asr/src/MainLoop.cc b/source/use_case/kws_asr/src/MainLoop.cc
index 5c1d0e0..f1d97a0 100644
--- a/source/use_case/kws_asr/src/MainLoop.cc
+++ b/source/use_case/kws_asr/src/MainLoop.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");
@@ -14,7 +14,6 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "hal.h"                    /* Brings in platform definitions. */
 #include "InputFiles.hpp"           /* For input images. */
 #include "Labels_micronetkws.hpp"   /* For MicroNetKws label strings. */
 #include "Labels_wav2letter.hpp"    /* For Wav2Letter label strings. */
@@ -24,8 +23,6 @@
 #include "Wav2LetterModel.hpp"      /* ASR model class for running inference. */
 #include "UseCaseCommonUtils.hpp"   /* Utils functions. */
 #include "UseCaseHandler.hpp"       /* Handlers for different user options. */
-#include "Wav2LetterPreprocess.hpp" /* ASR pre-processing class. */
-#include "Wav2LetterPostprocess.hpp"/* ASR post-processing class. */
 #include "log_macros.h"
 
 using KwsClassifier = arm::app::Classifier;
@@ -53,19 +50,8 @@
     fflush(stdout);
 }
 
-/** @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);
+/** @brief   Verify input and output tensor are of certain min dimensions. */
+static bool VerifyTensorDimensions(const arm::app::Model& model);
 
 void main_loop()
 {
@@ -84,61 +70,46 @@
     if (!asrModel.Init(kwsModel.GetAllocator())) {
         printf_err("Failed to initialise ASR model\n");
         return;
+    } else if (!VerifyTensorDimensions(asrModel)) {
+        printf_err("Model's input or output dimension verification failed\n");
+        return;
     }
 
-    /* Initialise ASR pre-processing. */
-    arm::app::audio::asr::Preprocess prep(
-            GetNumMfccFeatures(asrModel),
-            arm::app::asr::g_FrameLength,
-            arm::app::asr::g_FrameStride,
-            GetNumMfccFeatureVectors(asrModel));
-
-    /* Initialise ASR post-processing. */
-    const uint32_t outputCtxLen = GetOutputContextLen(asrModel, arm::app::asr::g_ctxLen);
-    const uint32_t blankTokenIdx = 28;
-    arm::app::audio::asr::Postprocess postp(
-            outputCtxLen,
-            GetOutputInnerLen(asrModel, outputCtxLen),
-            blankTokenIdx);
-
     /* Instantiate application context. */
     arm::app::ApplicationContext caseContext;
 
     arm::app::Profiler profiler{"kws_asr"};
     caseContext.Set<arm::app::Profiler&>("profiler", profiler);
-    caseContext.Set<arm::app::Model&>("kwsmodel", kwsModel);
-    caseContext.Set<arm::app::Model&>("asrmodel", asrModel);
+    caseContext.Set<arm::app::Model&>("kwsModel", kwsModel);
+    caseContext.Set<arm::app::Model&>("asrModel", asrModel);
     caseContext.Set<uint32_t>("clipIndex", 0);
     caseContext.Set<uint32_t>("ctxLen", arm::app::asr::g_ctxLen);  /* Left and right context length (MFCC feat vectors). */
-    caseContext.Set<int>("kwsframeLength", arm::app::kws::g_FrameLength);
-    caseContext.Set<int>("kwsframeStride", arm::app::kws::g_FrameStride);
-    caseContext.Set<float>("kwsscoreThreshold", arm::app::kws::g_ScoreThreshold);  /* Normalised score threshold. */
+    caseContext.Set<int>("kwsFrameLength", arm::app::kws::g_FrameLength);
+    caseContext.Set<int>("kwsFrameStride", arm::app::kws::g_FrameStride);
+    caseContext.Set<float>("kwsScoreThreshold", arm::app::kws::g_ScoreThreshold);  /* Normalised score threshold. */
     caseContext.Set<uint32_t >("kwsNumMfcc", arm::app::kws::g_NumMfcc);
     caseContext.Set<uint32_t >("kwsNumAudioWins", arm::app::kws::g_NumAudioWins);
 
-    caseContext.Set<int>("asrframeLength", arm::app::asr::g_FrameLength);
-    caseContext.Set<int>("asrframeStride", arm::app::asr::g_FrameStride);
-    caseContext.Set<float>("asrscoreThreshold", arm::app::asr::g_ScoreThreshold);  /* Normalised score threshold. */
+    caseContext.Set<int>("asrFrameLength", arm::app::asr::g_FrameLength);
+    caseContext.Set<int>("asrFrameStride", arm::app::asr::g_FrameStride);
+    caseContext.Set<float>("asrScoreThreshold", arm::app::asr::g_ScoreThreshold);  /* Normalised score threshold. */
 
     KwsClassifier kwsClassifier;  /* Classifier wrapper object. */
     arm::app::AsrClassifier asrClassifier;  /* Classifier wrapper object. */
-    caseContext.Set<arm::app::Classifier&>("kwsclassifier", kwsClassifier);
-    caseContext.Set<arm::app::AsrClassifier&>("asrclassifier", asrClassifier);
-
-    caseContext.Set<arm::app::audio::asr::Preprocess&>("preprocess", prep);
-    caseContext.Set<arm::app::audio::asr::Postprocess&>("postprocess", postp);
+    caseContext.Set<arm::app::Classifier&>("kwsClassifier", kwsClassifier);
+    caseContext.Set<arm::app::AsrClassifier&>("asrClassifier", asrClassifier);
 
     std::vector<std::string> asrLabels;
     arm::app::asr::GetLabelsVector(asrLabels);
     std::vector<std::string> kwsLabels;
     arm::app::kws::GetLabelsVector(kwsLabels);
-    caseContext.Set<const std::vector <std::string>&>("asrlabels", asrLabels);
-    caseContext.Set<const std::vector <std::string>&>("kwslabels", kwsLabels);
+    caseContext.Set<const std::vector <std::string>&>("asrLabels", asrLabels);
+    caseContext.Set<const std::vector <std::string>&>("kwsLabels", kwsLabels);
 
     /* KWS keyword that triggers ASR and associated checks */
-    std::string triggerKeyword = std::string("yes");
+    std::string triggerKeyword = std::string("no");
     if (std::find(kwsLabels.begin(), kwsLabels.end(), triggerKeyword) != kwsLabels.end()) {
-        caseContext.Set<const std::string &>("triggerkeyword", triggerKeyword);
+        caseContext.Set<const std::string &>("triggerKeyword", triggerKeyword);
     }
     else {
         printf_err("Selected trigger keyword not found in labels file\n");
@@ -196,50 +167,26 @@
     info("Main loop terminated.\n");
 }
 
-static uint32_t GetNumMfccFeatures(const arm::app::Model& model)
+static bool VerifyTensorDimensions(const arm::app::Model& model)
 {
+    /* Populate tensor related parameters. */
     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;
+    if (!inputTensor->dims) {
+        printf_err("Invalid input tensor dims\n");
+        return false;
+    } else if (inputTensor->dims->size < 3) {
+        printf_err("Input tensor dimension should be >= 3\n");
+        return false;
     }
 
     TfLiteTensor* outputTensor = model.GetOutputTensor(0);
-    const uint32_t outputRows = std::max(outputTensor->dims->data[ms_outputRowsIdx], 0);
+    if (!outputTensor->dims) {
+        printf_err("Invalid output tensor dims\n");
+        return false;
+    } else if (outputTensor->dims->size < 3) {
+        printf_err("Output tensor dimension should be >= 3\n");
+        return false;
+    }
 
-    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));
+    return true;
 }