Sentence examples for word recognition accuracy from inspiring English sources

The word recognition accuracy of the SI model is 91.54%.

Figure 7 Word recognition accuracy of the Tucker 3-mode based model, MLLR, and eigenvoice adaptation.

Figure 6 Word recognition accuracy of the probabilistic 2-mode based model, Tucker 3-mode based model, and MAPLR adaptation.

This improved phoneme prediction accuracy, when integrated into a standard large vocabulary continuous speech recognition (LVCSR) system through a word lattice rescoring framework, results in improved word recognition accuracy, which is better than previously reported word lattice rescoring results.

Results showed that listeners' word recognition accuracy for the acoustically degraded sentences was significantly higher when the target sentence was preceded by a conceptually related compared to a conceptually unrelated sentence.

The results showed that an average Transaction Success rate of more than 92% was obtained when speech recognisers exhibiting good Word Recognition Accuracy were coupled to suitable dialogue interfaces in the IVR system.

There was no significant difference between the patient groups in word and face recognition accuracy.

In the recognition task within the scanner both patient groups had significantly worse word and face recognition accuracy than control subjects.

Substantial amounts of resources are usually required to robustly develop a language model for an open vocabulary speech recognition system as out-of-vocabulary (OOV) words can hurt recognition accuracy.

A hybrid lexicon was utilized to alleviate the problem of OOV words and to improve recognition accuracy through additional linguistic information from multiple unit types.

In this paper, we apply a hybrid LM of word and sub-word units to a Thai LVCSR system with two goals in mind, to alleviate the problem of OOV words and to improve recognition accuracy, both in a resource-efficient way.