An Acoustic-Graphemic Model (AGM) and an Acoustic-Graphemic-Phonemic Model (AGPM) for analyzing speech at the phone level are introduced. An AGM is a multi-distribution deep neural network (MD-DNN) for which the inputs include a representation of acoustical features of an utterance and a corresponding graphemic transcription of the text. Inputs for an AGPM may include acoustical features, a graphemic transcription, and a canonical phonemic transition. Outputs from either an AGM or AGPM may include posterior probabilities for each phone in a set of recognizable phones. From the posterior probabilities, a most probable sequence of phones can be determined, e.g., using a Viterbi decoding algorithm and a state transition model that provides the posterior probability of a particular phone given a particular sequence of preceding phones.
ACOUSTIC-GRAPHEMIC MODEL AND ACOUSTIC-GRAPHEMIC-PHONEMIC MODEL FOR COMPUTER-AIDED PRONUNCIATION TRAINING AND SPEECH PROCESSING