Towards ASR Based on Hierarchical Posterior-Based Keyword Recognition

The paper presents an alternative approach to automatic recognition of speech in which each targeted word is classified by a separate binary classifier against all other sounds. No time alignment is done. To build a recognizer for N words, N parallel binary classifiers are applied. The system first estimates uniformly sampled posterior probabilities of phoneme classes, followed by a second step in which a rather long sliding time window is applied to the phoneme posterior estimates and its content is classified by an artificial neural network to yield posterior probability of the keyword. On a small vocabulary ASR task, the system still does not reach the performance of the state-of-the-art system but its conceptual simplicity, the ease of adding new target words, and its inherent resistance to out-of-vocabulary sounds may prove significant advantage in many applications