Classification of speech under stress using target driven features

Speech production variations due to perceptually induced stress contribute significantly to reduced speech processing performance. One approach for assessment of production variations due to stress is to formulate an objective classification of speaker stress based upon the acoustic speech signal. This study proposes an algorithm for estimation of the probability of perceptually induced stress. It is suggested that the resulting stress score could be integrated into robust speech processing algorithms to improve robustness in adverse conditions. First, results from a previous stress classification study are employed to motivate selection of a targeted set of speech features on a per phoneme and stress group level. Analysis of articulatory, excitation and cepstral based features is conducted using a previously established stressed speech database (Speech Under Simulated and Actual Stress (SUSAS)). Stress sensitive targeted feature sets are then selected across ten stress conditions (including Apache helicopter cockpit, Angry, Clear, Lombard effect, Loud, etc.) and incorporated into a new targeted neural network stress classifier. Second, the targeted feature stress classification system is then evaluated and shown to achieve closed speaker, open token classification rates of 91.0%. Finally, the proposed stress classification algorithm is incorporated into a stress directed speech recognition system, where separate hidden Markov model recognizers are trained for each stress condition. An improvement of + 10.1% and + 15.4% over conventionally trained neutral and multi-style trained recognizers is demonstrated using the new stress directed recognition approach. Apache Helikopter Cockpit, wiitend, klur, Lombard Effekt, hut, etc.) ausgew'ahlt und in ein neues stressklassifizierendes neuronales T Corresponding author. R&urn6 Les variations dans la production de parole dues au stress induit contribuent de mar&e significative 5 la reduction des performances des systemes de traitement de parole. Pour estimer ces variations, une approche consiste 'a Ctablir une classification objective du stress du locuteur, basee sur le signal acoustique. Cette etude propose un algorithme pour l'estimation de la probabilite du stress induit. Le taux de stress predit par cet algorithme peut &tre integre dans des algorithmes de traitement de parole afin d'augmenter leur robustesse dans des environnements difficiles. Les resultats d'une etude precedente sur la classification du stress sont d'abord utilises pour selectionner un ensemble de parametres de parole relatifs au phon&me et au type de stress. Une analyse des parametres articulatoires, d'excitation et cepstraux est conduite sur une base de don&es de parole sous stress (" Speech Under Simulated and Actual Stress " (SUSAS)). Les parametres sensibles …