Recurrent Neural Learning for Classifying Spoken Utterances

For telecommunications companies or banks, etc processing spontaneous lanaguage in helpdesk scenarios is important for automatic telephone interactions. However, the problem of understanding spontaneous spoken language is difficult. Learning techniques such as neural networks have the ability to learn in a robust manner. Recurrent networks have been used in neurocognitive or psycholinguistically oriented approaches of language processing. Here they are examined for their potential in a difficult spoken language classification task. This paper describes an approach to learning classification of recorded operator assistance telephone utterances. We explore simple recurrent networks using a large, unique telecommunication corpus of spontaneous spoken language. Performance of the network indicates that a simple recurrent network is quite useful for learning classification of spontaneous spoken language in a robust manner, which may lead to their use in helpdesk call routing.