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Determination of an emotional state through speech increases the amount of information associated with a speaker. It is therefore important to be able to detect and identify a speaker's emotional state or state of stress. The paper proposes an approach based on genetic algorithms to determine a set of features that will allow robust classification of emotional states. Starting from a vector of 462 features, a subset of features is obtained providing a good discrimination between neutral, angry, loud and Lombard states for the SUSAS simulated domain and between neutral and stressed states for the SUSAS actual domain.

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Discontinuous transmission based on speech/pause detection represents a valid solution to improve the spectral efficiency of new-generation wireless communication systems. In this context, robust Voice Activity Detection (VAD) algorithms are required, as traditional solutions present a high misclassification rate in the presence of the background noise typical of mobile environments. This paper presents a voice detection algorithm which is robust to noisy environments thanks to a new methodology adopted for the matching process. More specifically, the VAD proposed is based on a pattern recognition approach in which the matching phase is performed by a set of six fuzzy rules trained by means of a new hybrid learning tool. A series of objective tests performed on a large speech database, varying the signal-to-noise ratio, the types of background noise and the input signal level, showed that, as compared with the VAD recently standardized by ITU-T in Rec. G.729 Annex B, the Fuzzy VAD on average achieves an improvement in reduction both of the activity factor of about 25 % and of the clipping introduced of about 43 %. Informal listening tests also confirm an improvement in the perceived speech quality.

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