speakers of multiple genders and dialects [@timit]. Although the ultimate problem is sequence annotation, there is a derived multiclass classification problem of predicting the phonemic annotation associated with a short segment of audio. Such a classifier can be composed with standard sequence modeling techniques to produce an overall solution, which has made the multiclass problem a subject of research [@hinton2012improving; @hutchinson2012deep]. In this experiment we focus exclusively on the multiclass problem.

We use a standard preprocessing of TIMIT as our initial representation [@hutchinson2012deep]. Specifically the speech is converted into feature vectors via the first to twelfth Mel frequency cepstral coefficients and energy plus first and second temporal derivatives. This results in 39 coefficients per frame, which is concatenated with 5 preceding and 5 following frames to produce a 429 coefficient input to the classifier. The targets for the classifier are the 183 phone states (i.e., 61 phones each in 3 possible states).

We use the standard training, development, and test sets of TIMIT. As in previous experiments herein, hyperparameters are optimized on the development set (using cross-entropy as the objective), but unlike previous experiments we do not retrain with the development set once hyperparameters are determined, in correspondence with the 