Sentence examples for aligned targets from inspiring English sources

Relative reductions in WER of 8.2 and 6.2 % were obtained over a sequence-trained DNN using hard aligned targets and log-mel filterbank inputs.

One other slight modification is that early stopping is performed by monitoring the cross-entropy cost with soft targets from the teacher on a development set, rather than the frame classification rate or cross-entropy cost with hard aligned targets.

When reducing the size of the DNN to be much smaller (3.4 million parameters), 4.6 and 3.3 % relative reductions in WER are obtained compared to a small, sequence-trained DNN with hard aligned targets and log-mel filterbank inputs.

A small student DNN having 3.4 million parameters trained with soft targets from a larger, sequence-trained DNN obtained 4.6 and 3.3 % relative reductions in WER compared to a small DNN of the same size, sequence-trained with hard aligned targets and log-mel filterbank inputs.

Viewed in terms of parameter reduction, this is a 88.8 % reduction in parameters while actually achieving a 1.6 and 5.3 % relative reduction in WER compared to a much larger DNN sequence-trained with log-mel filterbank inputs and hard aligned targets.

Training a full-sized DNN (30.4 million parameters) with log-mel filterbank inputs using the outputs from a DNN of the same size which has been sequence-trained with fMLLR inputs, yields 8.2 and 6.2 % relative reductions in WER compared to a sequence-trained DNN using hard aligned targets and log-mel filterbank inputs.

The input data used are all available in the SQL database, which contains information such as Type Pair Frequencies, TPF, i.e. the number of times where the source and target types are aligned Target types per Source type, TpS, i.e. the number of target types a specific source type has been aligned to.

Here, S i is the probe sequence entropy, T ij is the melting temperature for a duplex of probe i and target j, B ij is the BLAST score, Q ij is the position of the aligned target on the probe sequence and a 0 through a 4 are the fitted model coefficients.

The general pattern of results found here is in close agreement with that found in Experiment 2. In that experiment, participants were allowed to see their arms, but the posture manipulation was smaller than here, and the aligned target eccentricities correspondingly different.

The assessment works for any alignment method, which aligns target sequences against structural templates.

Planned t tests treating each target eccentricity separately showed that cueing effects did not depend on whether the hands were aligned with targets [inner targets, t(23) = 1.3, p =.2; outer targets, t(23) = 1.3, p =.2].