This paper describes a research on topic identification in a real-world customer service telephone conversations between an agent and a customer. Separate hidden spaces are considered for agents, customers and the combination of them. The purpose is to separate semantic constituents from the speaker types and their possible relations. Probabilities of hidden topic features are then used by separate Gaussian classifiers to compute theme probabilities for each speaker type. A simple strategy, that does not require any additional parameter estimation, is introduced to classify themes with confidence indicators for each theme hypothesis. Experimental results on a real-life application show that the use of features from speaker type specific hidden spaces capture useful semantic contents with significantly superior performance with respect to independent word-based features or a single set of features. Experimental results also show that the proposed strategy makes it possible to perform surveys on collections of conversations by automatically selecting processed samples with high theme identification accuracy.