Speech Recognition in Noisy Environments with Convolutional Neural Networks

One of the biggest challenges in speech recognition today is its use on a daily basis, in which distortion and noise in the environment are present and hinder the recognition task. In the last thirty years, hundreds of methods for noise-robust recognition were proposed, each with its own advantages and disadvantages. In this paper, the use of convolutional neural networks (CNN) as acoustic models in automatic speech recognition systems (ASR) is proposed as an alternative to the classical recognition methods based on HMM without any noise-robust method applied. The experiment showed that the presented method reduces the equal error rate in word recognition tasks with additive noise.