Modeling Driver's Speech under Stress

Raul Fernandez, Rosalind W. Picard


We explore the use of features derived from multiresolution analysis of speech and the Teager Energy Operator for classification of drivers' speech under stressed conditions. We apply this set of features to a database of short speech utterances to create user-dependent discriminants of four stress categories. In addition, we address the problem of choosing a suitable temporal scale for representing categorical differences in the data. This leads to two modeling approaches. In the first approach, the dynamics of the feature set within the utterance are assumed to be important for the classification task. These features are then classified using dynamic Bayesian network (DBN) models as well as a model consisting of a mixture of hidden Markov models (M-HMM). In the second approach, we define an utterance-level feature set by taking the mean value of the features across the utterance. This feature set is then modeled with a support vector machine and a multilayer perceptron classifier. We compare the performance on the sparser and full dynamic representations against a chance-level performance of 25% and obtain the best performance with the speaker-dependent mixture model (96.4% on the training set, and 61.2% on a separate testing set). We also investigate how these models perform on the speaker-independent task. Although the performance of the speaker-independent models degrades with respect to the models trained on individual speakers, the mixture model still outperforms the competing models and achieves significantly better than random recognition (80.4% on the training set, and 51.2% on a separate testing set).

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