Uma abordagem híbrida CNN-HMM para reconhecimento de fala tolerante a ruídos de ambiente

Abstract

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 this task. In the last thirty years, hundreds of methods for noise-robust recognition were proposed, each with its own advantages and disadvantages. In this thesis, 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 Hidden Markov Models (HMM) without any noise-robust method applied. Experiments were performed with a audio set modified by additive and natural noises, and showed that the presented method reduces the Equal Error Rate (EER) and improves the acuracy of speech recognition in noisy environments when compared to traditional models of classifiation, indicating the robustness of the approach.