diff --git a/python/pyarmnn/examples/speech_recognition/audio_utils.py b/python/pyarmnn/examples/speech_recognition/audio_utils.py
index f03d2e1..1ac78e8 100644
--- a/python/pyarmnn/examples/speech_recognition/audio_utils.py
+++ b/python/pyarmnn/examples/speech_recognition/audio_utils.py
@@ -1,10 +1,9 @@
-# Copyright © 2020 Arm Ltd and Contributors. All rights reserved.
+# Copyright © 2021 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:
@@ -50,33 +49,6 @@
     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.
@@ -88,7 +60,6 @@
         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
@@ -109,25 +80,3 @@
     # Store the right context, we will need it after the last inference
     current_r_context = decode(output_result[0][0][0][right_context_start:], 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