Classification of Speaker Intoxication Using a Bidirectional Recurrent Neural Network

With the increasing popularity of deep learning approaches in the field of speech recognition and classification many of such problems are encountering a paradigm shift from classic approaches, such as hidden Markov models, to recurrent neural networks (RNN). In this paper we are going to examine that transition for the ALC corpus which had been used in the Interspeech 2011 Speaker State Challenge. Filter bank (FBANK) features are used alongside two types of bidirectional RNNs, each using gated recurrent units (GRU). Those models are used to classify the intoxication state of people just by recordings of their voices and outperform humans with state-of-the-art results