diff --git a/python/pyarmnn/examples/speech_recognition/audio_utils.py b/python/pyarmnn/examples/speech_recognition/audio_utils.py
new file mode 100644
index 0000000..a522a0e
--- /dev/null
+++ b/python/pyarmnn/examples/speech_recognition/audio_utils.py
@@ -0,0 +1,128 @@
+# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+# SPDX-License-Identifier: MIT
+
+"""Utilities for speech recognition apps."""
+
+import numpy as np
+import pyarmnn as ann
+
+
+def decode(model_output: np.ndarray, labels: dict) -> str:
+    """Decodes the integer encoded results from inference into a string.
+
+    Args:
+        model_output: Results from running inference.
+        labels: Dictionary of labels keyed on the classification index.
+
+    Returns:
+        Decoded string.
+    """
+    top1_results = [labels[np.argmax(row[0])] for row in model_output]
+    return filter_characters(top1_results)
+
+
+def filter_characters(results: list) -> str:
+    """Filters unwanted and duplicate characters.
+
+    Args:
+        results: List of top 1 results from inference.
+
+    Returns:
+        Final output string to present to user.
+    """
+    text = ""
+    for i in range(len(results)):
+        if results[i] == "$":
+            continue
+        elif i + 1 < len(results) and results[i] == results[i + 1]:
+            continue
+        else:
+            text += results[i]
+    return text
+
+
+def display_text(text: str):
+    """Presents the results on the console.
+
+    Args:
+        text: Results of performing ASR on the input audio data.
+    """
+    print(text, sep="", end="", flush=True)
+
+
+def quantize_input(data, input_binding_info):
+    """Quantize the float input to (u)int8 ready for inputting to model."""
+    if data.ndim != 2:
+        raise RuntimeError("Audio data must have 2 dimensions for quantization")
+
+    quant_scale = input_binding_info[1].GetQuantizationScale()
+    quant_offset = input_binding_info[1].GetQuantizationOffset()
+    data_type = input_binding_info[1].GetDataType()
+
+    if data_type == ann.DataType_QAsymmS8:
+        data_type = np.int8
+    elif data_type == ann.DataType_QAsymmU8:
+        data_type = np.uint8
+    else:
+        raise ValueError("Could not quantize data to required data type")
+
+    d_min = np.iinfo(data_type).min
+    d_max = np.iinfo(data_type).max
+
+    for row in range(data.shape[0]):
+        for col in range(data.shape[1]):
+            data[row, col] = (data[row, col] / quant_scale) + quant_offset
+            data[row, col] = np.clip(data[row, col], d_min, d_max)
+    data = data.astype(data_type)
+    return data
+
+
+def decode_text(is_first_window, labels, output_result):
+    """
+    Slices the text appropriately depending on the window, and decodes for wav2letter output.
+        * First run, take the left context, and inner context.
+        * Every other run, take the inner context.
+    Stores the current right context, and updates it for each inference. Will get used after last inference
+
+    Args:
+        is_first_window: Boolean to show if it is the first window we are running inference on
+        labels: the label set
+        output_result: the output from the inference
+        text: the current text string, to be displayed at the end
+    Returns:
+        current_r_context: the current right context
+        text: the current text string, with the latest output decoded and appended
+    """
+
+    if is_first_window:
+        # Since it's the first inference, keep the left context, and inner context, and decode
+        text = decode(output_result[0][0:472], labels)
+    else:
+        # Only decode the inner context
+        text = decode(output_result[0][49:472], labels)
+
+    # Store the right context, we will need it after the last inference
+    current_r_context = decode(output_result[0][473:521], labels)
+    return current_r_context, text
+
+
+def prepare_input_tensors(audio_data, input_binding_info, mfcc_preprocessor):
+    """
+    Takes a block of audio data, extracts the MFCC features, quantizes the array, and uses ArmNN to create the
+    input tensors.
+
+    Args:
+        audio_data: The audio data to process
+        mfcc_instance: the mfcc class instance
+        input_binding_info: the model input binding info
+        mfcc_preprocessor: the mfcc preprocessor instance
+    Returns:
+        input_tensors: the prepared input tensors, ready to be consumed by the ArmNN NetworkExecutor
+    """
+
+    data_type = input_binding_info[1].GetDataType()
+    input_tensor = mfcc_preprocessor.extract_features(audio_data)
+    if data_type != ann.DataType_Float32:
+        input_tensor = quantize_input(input_tensor, input_binding_info)
+    input_tensors = ann.make_input_tensors([input_binding_info], [input_tensor])
+    return input_tensors