In this paper, we address the problem of detecting sensitive events in speech signal such as exchange of credit card information. Although close in nature to the word spotting problem, variability in the linguistic content constituting an event and their composition makes event detection a harder task, especially in the context where it is applied such as call-center interaction. In this work we extend the hidden Markov model (HMM) based framework as used in word spotting to event detection, by constructing a network composed of HMM based acoustic models for event and garbage (non-event). Vocabularies specific to the event and non-event are used respectively to build the event and garbage models along with length constraints based on prior knowledge. Effectiveness of this approach is demonstrated by applying it to the problem of detecting credit card transaction event in real life conversations between agents and customers in call center. Our approach yield a false alarm rate of 17.0% and false miss rate of 12.5%.
['Shajith Ikbal', 'Tanveer A. Faruquie']
HMM based event detection in audio conversation