Sentence examples for posterior phone from inspiring English sources

One successful example is presented by Siniscalchi et al.[6], one of the best results reported on the phone TIMIT recognition task, where 15 broad articulatory classes are used to predict posterior phone probabilities and to rescore phone lattices.

As expected, a combination of broad-class posteriors with phone posteriors can be effective for enhancing both the correctness and accuracy rates in phone recognition.

The serial arrangement proposed provides several broad-class posteriors along with the phone posteriors.

The goal of a combination approach is to take advantage of the broad-class posteriors along with the phone posteriors in order to improve the global phone recognition performance.

State-level phone posterior probabilities are employed as features for the query and the utterance representation.

The HMM baseline system employed discriminative training, neural-network-derived phone posterior probability features, as well as ensemble acoustic models, etc.

The field of acoustic modeling for ASR has seen a lot of research on the usage of neural networks that are able to estimate phone posterior distributions.

The DTW algorithm is used for query detection from the state-level phone posterior probabilities that represent each query and utterance frame.

In that case, the speech features were either the estimated phone posterior probabilities (usually decorrelated and with a reduced dimensionality (tandem)) or the activations of a narrow hidden layer (bottleneck).

The global phone posteriors are found by combining the corresponding outputs of all output layers.

Results  of only  speech/music discrimination: 98.0% Ajmera et  al., 2003 [14] Automatic transcription of  broadcast news Averaged  entropy measure  and "dynamism"  estimated at  the  output  of  a multilayer  perceptron (MLP) trained to emit posterior  probabilities of phones.