diff --git a/samples/SpeechRecognition/src/Wav2LetterPreprocessor.cpp b/samples/SpeechRecognition/src/Wav2LetterPreprocessor.cpp
new file mode 100644
index 0000000..9329d5e
--- /dev/null
+++ b/samples/SpeechRecognition/src/Wav2LetterPreprocessor.cpp
@@ -0,0 +1,187 @@
+//
+// Copyright © 2021 Arm Ltd and Contributors. All rights reserved.
+// SPDX-License-Identifier: MIT
+//
+#include "MathUtils.hpp"
+#include <cstring>
+#include <cmath>
+#include <numeric>
+#include <algorithm>
+#include <memory>
+#include "Wav2LetterPreprocessor.hpp"
+#include "Wav2LetterMFCC.hpp"
+
+float Wav2LetterPreprocessor::GetMean(Array2d<float>& vec)
+{
+    return MathUtils::MeanF32(vec.begin(), vec.totalSize());
+}
+
+float Wav2LetterPreprocessor::GetStdDev(Array2d<float>& vec, const float mean)
+{
+    return MathUtils::StdDevF32(vec.begin(), vec.totalSize(), mean);
+}
+
+void Wav2LetterPreprocessor::NormaliseVec(Array2d<float>& vec)
+{
+    auto mean = Wav2LetterPreprocessor::GetMean(vec);
+    auto stddev = Wav2LetterPreprocessor::GetStdDev(vec, mean);
+
+    if (stddev == 0)
+    {
+        std::fill(vec.begin(), vec.end(), 0);
+    }
+    else
+    {
+        const float stddevInv = 1.f/stddev;
+        const float normalisedMean = mean/stddev;
+
+        auto NormalisingFunction = [=](float &value) {
+            value = value * stddevInv - normalisedMean;
+        };
+        std::for_each(vec.begin(), vec.end(), NormalisingFunction);
+    }
+}
+
+void Wav2LetterPreprocessor::Normalise()
+{
+    Wav2LetterPreprocessor::NormaliseVec(this->m_mfccBuf);
+    Wav2LetterPreprocessor::NormaliseVec(this->m_delta1Buf);
+    Wav2LetterPreprocessor::NormaliseVec(this->m_delta2Buf);
+}
+
+float Wav2LetterPreprocessor::GetQuantElem(
+        const float     elem,
+        const float     quantScale,
+        const int       quantOffset,
+        const float     minVal,
+        const float     maxVal)
+{
+    float val = std::round((elem/quantScale) + quantOffset);
+    float returnVal = std::min<float>(std::max<float>(val, minVal), maxVal);
+    return returnVal;
+}
+
+bool Wav2LetterPreprocessor::Invoke(const float*  audioData, const uint32_t  audioDataLen, std::vector<int8_t>& output,
+                                     int quantOffset, float quantScale)
+{
+    this->m_window = SlidingWindow<const float>(
+            audioData, audioDataLen,
+            this->m_windowLen, this->m_windowStride);
+
+    uint32_t mfccBufIdx = 0;
+
+    // Init buffers with 0
+    std::fill(m_mfccBuf.begin(), m_mfccBuf.end(), 0.f);
+    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 float* mfccWindow = this->m_window.Next();
+        auto mfccAudioData = std::vector<float>(
+                mfccWindow,
+                mfccWindow + this->m_windowLen);
+
+        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];
+        }
+        ++mfccBufIdx;
+    }
+
+    // Pad MFCC if needed by repeating last feature vector 
+    while (mfccBufIdx != this->m_mfcc->m_params.m_numMfccVectors)
+    {
+        memcpy(&this->m_mfccBuf(0, mfccBufIdx),
+               &this->m_mfccBuf(0, mfccBufIdx - 1), sizeof(float) * this->m_mfcc->m_params.m_numMfccFeatures);
+        ++mfccBufIdx;
+    }
+
+    // Compute first and second order deltas from MFCCs 
+    Wav2LetterPreprocessor::ComputeDeltas(this->m_mfccBuf,
+                        this->m_delta1Buf,
+                        this->m_delta2Buf);
+
+    // Normalise 
+    this->Normalise();
+
+    return this->Quantise<int8_t>(output.data(), quantOffset, quantScale);
+}
+
+bool Wav2LetterPreprocessor::ComputeDeltas(Array2d<float>& mfcc,
+                                           Array2d<float>& delta1,
+                                           Array2d<float>& delta2)
+{
+    const std::vector <float> delta1Coeffs =
+            {6.66666667e-02,  5.00000000e-02,  3.33333333e-02,
+             1.66666667e-02, -3.46944695e-18, -1.66666667e-02,
+             -3.33333333e-02, -5.00000000e-02, -6.66666667e-02};
+
+    const std::vector <float> delta2Coeffs =
+            {0.06060606,      0.01515152,     -0.01731602,
+             -0.03679654,     -0.04329004,     -0.03679654,
+             -0.01731602,      0.01515152,      0.06060606};
+
+    if (delta1.size(0) == 0 || delta2.size(0) != delta1.size(0) ||
+        mfcc.size(0) == 0 || mfcc.size(1) == 0)
+    {
+        return false;
+    }
+
+    // Get the middle index; coeff vec len should always be odd 
+    const size_t coeffLen = delta1Coeffs.size();
+    const size_t fMidIdx = (coeffLen - 1)/2;
+    const size_t numFeatures = mfcc.size(0);
+    const size_t numFeatVectors = mfcc.size(1);
+
+    // iterate through features in MFCC vector
+    for (size_t i = 0; i < numFeatures; ++i)
+    {
+        /* for each feature, iterate through time (t) samples representing feature evolution and
+        * calculate d/dt and d^2/dt^2, using 1d convolution with differential kernels.
+        * Convolution padding = valid, result size is `time length - kernel length + 1`.
+        * The result is padded with 0 from both sides to match the size of initial time samples data.
+        *
+        * For the small filter, conv1d implementation as a simple loop is efficient enough.
+        * Filters of a greater size would need CMSIS-DSP functions to be used, like arm_fir_f32.
+        */
+
+        for (size_t j = fMidIdx; j < numFeatVectors - fMidIdx; ++j)
+        {
+            float d1 = 0;
+            float d2 = 0;
+            const size_t mfccStIdx = j - fMidIdx;
+
+            for (size_t k = 0, m = coeffLen - 1; k < coeffLen; ++k, --m)
+            {
+
+                d1 +=  mfcc(i,mfccStIdx + k) * delta1Coeffs[m];
+                d2 +=  mfcc(i,mfccStIdx + k) * delta2Coeffs[m];
+            }
+
+            delta1(i,j) = d1;
+            delta2(i,j) = d2;
+        }
+    }
+
+    return true;
+}
+
+Wav2LetterPreprocessor::Wav2LetterPreprocessor(const uint32_t  windowLen,
+                                               const uint32_t  windowStride,
+                                               std::unique_ptr<Wav2LetterMFCC> mfccInst):
+    m_mfcc(std::move(mfccInst)),
+    m_mfccBuf(m_mfcc->m_params.m_numMfccFeatures, m_mfcc->m_params.m_numMfccVectors),
+    m_delta1Buf(m_mfcc->m_params.m_numMfccFeatures, m_mfcc->m_params.m_numMfccVectors),
+    m_delta2Buf(m_mfcc->m_params.m_numMfccFeatures, m_mfcc->m_params.m_numMfccVectors),
+    m_windowLen(windowLen),
+    m_windowStride(windowStride) 
+{
+    if (m_mfcc->m_params.m_numMfccFeatures > 0 && windowLen > 0) 
+    {
+        this->m_mfcc->Init();
+    }
+    std::fill(m_mfccBuf.begin(), m_mfccBuf.end(), 0.f);
+}
\ No newline at end of file