Error-correcting training for phoneme spotting

Error-correcting training is extended to spotting algorithms. An error measure for spotting tasks is defined, and the derivatives of that error with respect to the parameters of the spotter are derived. The derivatives are computed by modifying the backward pass of the forward-backward algorithm. The training algorithm is applicable to any hidden Markov model (HMM)-based classification task in which a frame-by-frame classifier error can be defined. Here it is applied to phoneme spotting. Preliminary experimental results have been obtained for spotting the phonemes |r|, |s|, |t| in the TIMIT database. Error-correcting training generally improved the spotter accuracy, reducing the miss rate by up to 30%