Robust Speech Recognition Training via Duration and Spectral Based Stress Token Generation

It is known that speech recognition performance degrades if systems are not trained and tested under similar speaking conditions. This is particularly true if a speaker is exposed to high demanding workload stress or noise. For current recognition systems to be successful in applications susceptible to stress, speech recognizers should address the adverse conditions experienced by the user. In this paper, we consider the problem of improved recognition training for speech recognition for various stressed speaking conditions (e.g., slow, loud, and Lombard effect speaking styles). The main objective is to devise a training procedure which produces a hidden Markov model recognizer which better characterizes a given stressed speaking style, without the need for directly collecting such stressed data. The novel approach is to construct a word production model using a previously suggested source generator framework [6], by employing knowledge of the statistical nature of duration and spectra..