diff --git a/source/use_case/kws_asr/src/AsrClassifier.cc b/source/use_case/kws_asr/src/AsrClassifier.cc
index bc86e09..f1fa6f1 100644
--- a/source/use_case/kws_asr/src/AsrClassifier.cc
+++ b/source/use_case/kws_asr/src/AsrClassifier.cc
@@ -21,13 +21,17 @@
 #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)
+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];
 
+    if (nLetters != labels.size()) {
+        printf("Output size doesn't match the labels' size\n");
+        return false;
+    }
 
     /* NOTE: tensor's size verification against labels should be
      *       checked by the calling/public function. */
@@ -42,7 +46,7 @@
 
     /* 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);
+        std::pair<T, uint32_t> top_1 = std::make_pair(tensorData[row], 0);
 
         for (uint32_t j = 1; j < nLetters; ++j) {
             if (top_1.first < tensorData[row + j]) {
@@ -59,12 +63,12 @@
 
     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);
+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,
@@ -105,16 +109,16 @@
 
         switch (outputTensor->type) {
             case kTfLiteUInt8:
-                resultState = this->_GetTopResults<uint8_t>(
-                                        outputTensor, vecResults,
-                                        labels, quantParams.scale,
-                                        quantParams.offset);
+                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);
+                resultState = this->GetTopResults<int8_t>(
+                        outputTensor, vecResults,
+                        labels, quantParams.scale,
+                        quantParams.offset);
                 break;
             default:
                 printf_err("Tensor type %s not supported by classifier\n",
diff --git a/source/use_case/kws_asr/src/UseCaseHandler.cc b/source/use_case/kws_asr/src/UseCaseHandler.cc
index a428210..1edc7c4 100644
--- a/source/use_case/kws_asr/src/UseCaseHandler.cc
+++ b/source/use_case/kws_asr/src/UseCaseHandler.cc
@@ -52,13 +52,7 @@
     * @brief           Helper function to increment current audio clip index
     * @param[in,out]   ctx     pointer to the application context object
     **/
-    static void _IncrementAppCtxClipIdx(ApplicationContext& ctx);
-
-    /**
-    * @brief           Helper function to increment current audio clip index
-    * @param[in,out]   ctx     pointer to the application context object
-    **/
-    static void _IncrementAppCtxClipIdx(ApplicationContext& ctx);
+    static void IncrementAppCtxClipIdx(ApplicationContext& ctx);
 
     /**
      * @brief           Helper function to set the audio clip index
@@ -66,7 +60,7 @@
      * @param[in]       idx     value to be set
      * @return          true if index is set, false otherwise
      **/
-    static bool _SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
+    static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx);
 
     /**
      * @brief           Presents kws inference results using the data presentation
@@ -77,7 +71,7 @@
      *                              Otherwise, this can be passed in as 0.
      * @return          true if successful, false otherwise
      **/
-    static bool _PresentInferenceResult(hal_platform& platform, std::vector<arm::app::kws::KwsResult>& results);
+    static bool PresentInferenceResult(hal_platform& platform, std::vector<arm::app::kws::KwsResult>& results);
 
     /**
      * @brief           Presents asr inference results using the data presentation
@@ -88,7 +82,7 @@
      *                              Otherwise, this can be passed in as 0.
      * @return          true if successful, false otherwise
      **/
-    static bool _PresentInferenceResult(hal_platform& platform, std::vector<arm::app::asr::AsrResult>& results);
+    static bool PresentInferenceResult(hal_platform& platform, std::vector<arm::app::asr::AsrResult>& results);
 
     /**
      * @brief Returns a function to perform feature calculation and populates input tensor data with
@@ -212,7 +206,7 @@
         std::string str_inf{"Running KWS inference... "};
         platform.data_psn->present_data_text(
                             str_inf.c_str(), str_inf.size(),
-                            dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+                            dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
 
         info("Running KWS inference on audio clip %u => %s\n",
              currentIndex, get_filename(currentIndex));
@@ -279,9 +273,9 @@
         str_inf = std::string(str_inf.size(), ' ');
         platform.data_psn->present_data_text(
                             str_inf.c_str(), str_inf.size(),
-                            dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+                            dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
 
-        if (!_PresentInferenceResult(platform, kwsResults)) {
+        if (!PresentInferenceResult(platform, kwsResults)) {
             return output;
         }
 
@@ -375,7 +369,7 @@
         std::string str_inf{"Running ASR inference... "};
         platform.data_psn->present_data_text(
                 str_inf.c_str(), str_inf.size(),
-                dataPsnTxtInfStartX, dataPsnTxtInfStartY, 0);
+                dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
 
         size_t asrInferenceWindowLen = asrAudioParamsWinLen;
 
@@ -425,7 +419,7 @@
                         str_inf.c_str(), str_inf.size(),
                         dataPsnTxtInfStartX, dataPsnTxtInfStartY, false);
         }
-        if (!_PresentInferenceResult(platform, asrResults)) {
+        if (!PresentInferenceResult(platform, asrResults)) {
             return false;
         }
 
@@ -442,7 +436,7 @@
 
         /* If the request has a valid size, set the audio index. */
         if (clipIndex < NUMBER_OF_FILES) {
-            if (!_SetAppCtxClipIdx(ctx, clipIndex)) {
+            if (!SetAppCtxClipIdx(ctx, clipIndex)) {
                 return false;
             }
         }
@@ -463,14 +457,14 @@
                 }
             }
 
-            _IncrementAppCtxClipIdx(ctx);
+            IncrementAppCtxClipIdx(ctx);
 
         } while (runAll && ctx.Get<uint32_t>("clipIndex") != startClipIdx);
 
         return true;
     }
 
-    static void _IncrementAppCtxClipIdx(ApplicationContext& ctx)
+    static void IncrementAppCtxClipIdx(ApplicationContext& ctx)
     {
         auto curAudioIdx = ctx.Get<uint32_t>("clipIndex");
 
@@ -482,7 +476,7 @@
         ctx.Set<uint32_t>("clipIndex", curAudioIdx);
     }
 
-    static bool _SetAppCtxClipIdx(ApplicationContext& ctx, const uint32_t idx)
+    static bool SetAppCtxClipIdx(ApplicationContext& ctx, uint32_t idx)
     {
         if (idx >= NUMBER_OF_FILES) {
             printf_err("Invalid idx %u (expected less than %u)\n",
@@ -493,8 +487,8 @@
         return true;
     }
 
-    static bool _PresentInferenceResult(hal_platform& platform,
-                std::vector<arm::app::kws::KwsResult>& results)
+    static bool PresentInferenceResult(hal_platform& platform,
+                                       std::vector<arm::app::kws::KwsResult>& results)
     {
         constexpr uint32_t dataPsnTxtStartX1 = 20;
         constexpr uint32_t dataPsnTxtStartY1 = 30;
@@ -510,7 +504,7 @@
             std::string topKeyword{"<none>"};
             float score = 0.f;
 
-            if (results[i].m_resultVec.size()) {
+            if (!results[i].m_resultVec.empty()) {
                 topKeyword = results[i].m_resultVec[0].m_label;
                 score = results[i].m_resultVec[0].m_normalisedVal;
             }
@@ -538,7 +532,7 @@
         return true;
     }
 
-    static bool _PresentInferenceResult(hal_platform& platform, std::vector<arm::app::asr::AsrResult>& results)
+    static bool PresentInferenceResult(hal_platform& platform, std::vector<arm::app::asr::AsrResult>& results)
     {
         constexpr uint32_t dataPsnTxtStartX1 = 20;
         constexpr uint32_t dataPsnTxtStartY1 = 80;
@@ -587,8 +581,8 @@
      **/
     template<class T>
     std::function<void (std::vector<int16_t>&, size_t, bool, size_t)>
-    _FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
-                 std::function<std::vector<T> (std::vector<int16_t>& )> compute)
+    FeatureCalc(TfLiteTensor* inputTensor, size_t cacheSize,
+                std::function<std::vector<T> (std::vector<int16_t>& )> compute)
     {
         /* Feature cache to be captured by lambda function. */
         static std::vector<std::vector<T>> featureCache = std::vector<std::vector<T>>(cacheSize);
@@ -621,24 +615,24 @@
     }
 
     template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
-    _FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
+    FeatureCalc<int8_t>(TfLiteTensor* inputTensor,
+                        size_t cacheSize,
+                        std::function<std::vector<int8_t> (std::vector<int16_t>& )> compute);
+
+    template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
+    FeatureCalc<uint8_t>(TfLiteTensor* inputTensor,
                          size_t cacheSize,
-                         std::function<std::vector<int8_t> (std::vector<int16_t>& )> compute);
+                         std::function<std::vector<uint8_t> (std::vector<int16_t>& )> compute);
 
     template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
-    _FeatureCalc<uint8_t>(TfLiteTensor* inputTensor,
-                          size_t cacheSize,
-                          std::function<std::vector<uint8_t> (std::vector<int16_t>& )> compute);
-
-    template std::function<void (std::vector<int16_t>&, size_t , bool, size_t)>
-    _FeatureCalc<int16_t>(TfLiteTensor* inputTensor,
-                          size_t cacheSize,
-                          std::function<std::vector<int16_t> (std::vector<int16_t>& )> compute);
+    FeatureCalc<int16_t>(TfLiteTensor* inputTensor,
+                         size_t cacheSize,
+                         std::function<std::vector<int16_t> (std::vector<int16_t>& )> compute);
 
     template std::function<void(std::vector<int16_t>&, size_t, bool, size_t)>
-    _FeatureCalc<float>(TfLiteTensor* inputTensor,
-                        size_t cacheSize,
-                        std::function<std::vector<float>(std::vector<int16_t>&)> compute);
+    FeatureCalc<float>(TfLiteTensor* inputTensor,
+                       size_t cacheSize,
+                       std::function<std::vector<float>(std::vector<int16_t>&)> compute);
 
 
     static std::function<void (std::vector<int16_t>&, int, bool, size_t)>
@@ -656,35 +650,35 @@
 
             switch (inputTensor->type) {
                 case kTfLiteInt8: {
-                    mfccFeatureCalc = _FeatureCalc<int8_t>(inputTensor,
-                                                           cacheSize,
-                                                           [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
-                                                               return mfcc.MfccComputeQuant<int8_t>(audioDataWindow,
-                                                                                                    quantScale,
-                                                                                                    quantOffset);
-                                                           }
+                    mfccFeatureCalc = FeatureCalc<int8_t>(inputTensor,
+                                                          cacheSize,
+                                                          [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+                                                              return mfcc.MfccComputeQuant<int8_t>(audioDataWindow,
+                                                                                                   quantScale,
+                                                                                                   quantOffset);
+                                                          }
                     );
                     break;
                 }
                 case kTfLiteUInt8: {
-                    mfccFeatureCalc = _FeatureCalc<uint8_t>(inputTensor,
-                                                            cacheSize,
-                                                            [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
-                                                                return mfcc.MfccComputeQuant<uint8_t>(audioDataWindow,
-                                                                                                      quantScale,
-                                                                                                      quantOffset);
-                                                            }
+                    mfccFeatureCalc = FeatureCalc<uint8_t>(inputTensor,
+                                                           cacheSize,
+                                                           [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+                                                               return mfcc.MfccComputeQuant<uint8_t>(audioDataWindow,
+                                                                                                     quantScale,
+                                                                                                     quantOffset);
+                                                           }
                     );
                     break;
                 }
                 case kTfLiteInt16: {
-                    mfccFeatureCalc = _FeatureCalc<int16_t>(inputTensor,
-                                                            cacheSize,
-                                                            [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
-                                                                return mfcc.MfccComputeQuant<int16_t>(audioDataWindow,
-                                                                                                      quantScale,
-                                                                                                      quantOffset);
-                                                            }
+                    mfccFeatureCalc = FeatureCalc<int16_t>(inputTensor,
+                                                           cacheSize,
+                                                           [=, &mfcc](std::vector<int16_t>& audioDataWindow) {
+                                                               return mfcc.MfccComputeQuant<int16_t>(audioDataWindow,
+                                                                                                     quantScale,
+                                                                                                     quantOffset);
+                                                           }
                     );
                     break;
                 }
@@ -694,11 +688,11 @@
 
 
         } else {
-            mfccFeatureCalc = mfccFeatureCalc = _FeatureCalc<float>(inputTensor,
-                                                                    cacheSize,
-                                                                    [&mfcc](std::vector<int16_t>& audioDataWindow) {
-                                                                        return mfcc.MfccCompute(audioDataWindow);
-                                                                    });
+            mfccFeatureCalc = mfccFeatureCalc = FeatureCalc<float>(inputTensor,
+                                                                   cacheSize,
+                                                                   [&mfcc](std::vector<int16_t>& audioDataWindow) {
+                                                                       return mfcc.MfccCompute(audioDataWindow);
+                                                                   });
         }
         return mfccFeatureCalc;
     }
diff --git a/source/use_case/kws_asr/src/Wav2LetterMfcc.cc b/source/use_case/kws_asr/src/Wav2LetterMfcc.cc
index 80e4a26..ae9e57a 100644
--- a/source/use_case/kws_asr/src/Wav2LetterMfcc.cc
+++ b/source/use_case/kws_asr/src/Wav2LetterMfcc.cc
@@ -27,8 +27,8 @@
     bool Wav2LetterMFCC::ApplyMelFilterBank(
             std::vector<float>&                 fftVec,
             std::vector<std::vector<float>>&    melFilterBank,
-            std::vector<int32_t>&               filterBankFilterFirst,
-            std::vector<int32_t>&               filterBankFilterLast,
+            std::vector<uint32_t>&              filterBankFilterFirst,
+            std::vector<uint32_t>&              filterBankFilterLast,
             std::vector<float>&                 melEnergies)
     {
         const size_t numBanks = melEnergies.size();
@@ -41,11 +41,14 @@
 
         for (size_t bin = 0; bin < numBanks; ++bin) {
             auto filterBankIter = melFilterBank[bin].begin();
-            float melEnergy = 1e-10;  /* Avoid log of zero at later stages, same value used in librosa. */
-            const int32_t firstIndex = filterBankFilterFirst[bin];
-            const int32_t lastIndex = filterBankFilterLast[bin];
+            auto end = melFilterBank[bin].end();
+            /* Avoid log of zero at later stages, same value used in librosa.
+             * The number was used during our default wav2letter model training. */
+            float melEnergy = 1e-10;
+            const uint32_t firstIndex = filterBankFilterFirst[bin];
+            const uint32_t lastIndex = std::min<uint32_t>(filterBankFilterLast[bin], fftVec.size() - 1);
 
-            for (int32_t i = firstIndex; i <= lastIndex; ++i) {
+            for (uint32_t i = firstIndex; i <= lastIndex && filterBankIter != end; ++i) {
                 melEnergy += (*filterBankIter++ * fftVec[i]);
             }
 
@@ -73,7 +76,7 @@
 
         /* Scale the log values and get the max. */
         for (auto iterM = melEnergies.begin(), iterL = vecLogEnergies.begin();
-                  iterM != melEnergies.end(); ++iterM, ++iterL) {
+                  iterM != melEnergies.end() && iterL != vecLogEnergies.end(); ++iterM, ++iterL) {
 
             *iterM = *iterL * multiplier;
 
@@ -86,8 +89,8 @@
         /* Clamp the mel energies. */
         constexpr float maxDb = 80.0;
         const float clampLevelLowdB = maxMelEnergy - maxDb;
-        for (auto iter = melEnergies.begin(); iter != melEnergies.end(); ++iter) {
-            *iter = std::max(*iter, clampLevelLowdB);
+        for (float & melEnergie : melEnergies) {
+            melEnergie = std::max(melEnergie, clampLevelLowdB);
         }
     }
 
diff --git a/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc b/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc
index b173968..ee3aba0 100644
--- a/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc
+++ b/source/use_case/kws_asr/src/Wav2LetterPostprocess.cc
@@ -38,13 +38,13 @@
                             const bool      lastIteration)
     {
         /* Basic checks. */
-        if (!this->_IsInputValid(tensor, axisIdx)) {
+        if (!this->IsInputValid(tensor, axisIdx)) {
             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);
+        const uint32_t elemSz = this->GetTensorElementSize(tensor);
 
         /* Other sanity checks. */
         if (0 == elemSz) {
@@ -58,9 +58,10 @@
         /* 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);
+                return this->EraseSectionsRowWise(ptrData,
+                                                  elemSz *
+                                                  tensor->dims->data[arm::app::Wav2LetterModel::ms_outputColsIdx],
+                                                  lastIteration);
             default:
                 printf_err("Unsupported axis index: %u\n", axisIdx);
         }
@@ -68,8 +69,8 @@
         return false;
     }
 
-    bool Postprocess::_IsInputValid(TfLiteTensor*  tensor,
-                                    const uint32_t axisIdx) const
+    bool Postprocess::IsInputValid(TfLiteTensor*  tensor,
+                                   const uint32_t axisIdx) const
     {
         if (nullptr == tensor) {
             return false;
@@ -91,7 +92,7 @@
         return true;
     }
 
-    uint32_t Postprocess::_GetTensorElementSize(TfLiteTensor*  tensor)
+    uint32_t Postprocess::GetTensorElementSize(TfLiteTensor*  tensor)
     {
         switch(tensor->type) {
             case kTfLiteUInt8:
@@ -112,7 +113,7 @@
         return 0;
     }
 
-    bool Postprocess::_EraseSectionsRowWise(
+    bool Postprocess::EraseSectionsRowWise(
                         uint8_t*         ptrData,
                         const uint32_t   strideSzBytes,
                         const bool       lastIteration)
diff --git a/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc b/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc
index 613ddb0..8251396 100644
--- a/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc
+++ b/source/use_case/kws_asr/src/Wav2LetterPreprocess.cc
@@ -88,12 +88,12 @@
         }
 
         /* Compute first and second order deltas from MFCCs. */
-        this->_ComputeDeltas(this->_m_mfccBuf,
-                             this->_m_delta1Buf,
-                             this->_m_delta2Buf);
+        this->ComputeDeltas(this->_m_mfccBuf,
+                            this->_m_delta1Buf,
+                            this->_m_delta2Buf);
 
         /* Normalise. */
-        this->_Normalise();
+        this->Normalise();
 
         /* Quantise. */
         QuantParams quantParams = GetTensorQuantParams(tensor);
@@ -105,11 +105,11 @@
 
         switch(tensor->type) {
             case kTfLiteUInt8:
-                return this->_Quantise<uint8_t>(
+                return this->Quantise<uint8_t>(
                         tflite::GetTensorData<uint8_t>(tensor), tensor->bytes,
                         quantParams.scale, quantParams.offset);
             case kTfLiteInt8:
-                return this->_Quantise<int8_t>(
+                return this->Quantise<int8_t>(
                         tflite::GetTensorData<int8_t>(tensor), tensor->bytes,
                         quantParams.scale, quantParams.offset);
             default:
@@ -120,9 +120,9 @@
         return false;
     }
 
-    bool Preprocess::_ComputeDeltas(Array2d<float>& mfcc,
-                                    Array2d<float>& delta1,
-                                    Array2d<float>& delta2)
+    bool Preprocess::ComputeDeltas(Array2d<float>& mfcc,
+                                   Array2d<float>& delta1,
+                                   Array2d<float>& delta2)
     {
         const std::vector <float> delta1Coeffs =
             {6.66666667e-02,  5.00000000e-02,  3.33333333e-02,
@@ -175,20 +175,20 @@
         return true;
     }
 
-    float Preprocess::_GetMean(Array2d<float>& vec)
+    float Preprocess::GetMean(Array2d<float>& vec)
     {
         return math::MathUtils::MeanF32(vec.begin(), vec.totalSize());
     }
 
-    float Preprocess::_GetStdDev(Array2d<float>& vec, const float mean)
+    float Preprocess::GetStdDev(Array2d<float>& vec, const float mean)
     {
         return math::MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean);
     }
 
-    void Preprocess::_NormaliseVec(Array2d<float>& vec)
+    void Preprocess::NormaliseVec(Array2d<float>& vec)
     {
-        auto mean = Preprocess::_GetMean(vec);
-        auto stddev = Preprocess::_GetStdDev(vec, mean);
+        auto mean = Preprocess::GetMean(vec);
+        auto stddev = Preprocess::GetStdDev(vec, mean);
 
         debug("Mean: %f, Stddev: %f\n", mean, stddev);
         if (stddev == 0) {
@@ -204,14 +204,14 @@
         }
     }
 
-    void Preprocess::_Normalise()
+    void Preprocess::Normalise()
     {
-        Preprocess::_NormaliseVec(this->_m_mfccBuf);
-        Preprocess::_NormaliseVec(this->_m_delta1Buf);
-        Preprocess::_NormaliseVec(this->_m_delta2Buf);
+        Preprocess::NormaliseVec(this->_m_mfccBuf);
+        Preprocess::NormaliseVec(this->_m_delta1Buf);
+        Preprocess::NormaliseVec(this->_m_delta2Buf);
     }
 
-    float Preprocess::_GetQuantElem(
+    float Preprocess::GetQuantElem(
                 const float     elem,
                 const float     quantScale,
                 const int       quantOffset,