Speech and Language Impairment Detection by Means of AI-Driven Audio-Based Techniques

Speech and Language Impairments (SLI) affect a large and heterogeneous group of people. With our work, we propose a novel, easy, and immediate detection tool to help diagnose people who suffer from SLI using speech audio signals, along with a new dataset containing English speakers affected by SLI. In this work, we experiment with feature extraction methods such as Mel Spectrogram and wav2vec 2.0, as well as classification methods such as SVM, CNN, and linear neural networks. We also work on data audio augmentation trying to overcome the very common limitations imposed by data scarcity in the medical field. The overall results indicate that the wav2vec 2.0 feature extractor, paired with a linear classifier, provides the best performance with a reasonably high accuracy of over 96%.