  Automatic speech recognition systems are part of people's daily lives,
embedded in personal assistants and mobile phones, helping as a facilitator for
human-machine interaction while allowing access to information in a practically
intuitive way. Such systems are usually implemented using machine learning
techniques, especially with deep neural networks. Even with its high
performance in the task of transcribing text from speech, few works address the
issue of its recognition in noisy environments and, usually, the datasets used
do not contain noisy audio examples, while only mitigating this issue using
data augmentation techniques. This work aims to present the process of building
a dataset of noisy audios, in a specific case of degenerated audios due to
interference, commonly present in radio transmissions. Additionally, we present
initial results of a classifier that uses such data for evaluation, indicating
the benefits of using this dataset in the recognizer's training process. Such
recognizer achieves an average result of 0.4116 in terms of character error
rate in the noisy set (SNR = 30).
