A team of scientists used machine learning to develop an improved model for speech recognition. This is reported by Venture Beat.
Previously, scientists manually identified phonological similarities between units of language in general American English and the pronunciation dictionary of the Carnegie Mellon University. To create an improved model, they went non-standard way and allowed it to automatically form the rules. Then, it compared the resulting unique list with a set of examples from George Mason University's speech accents archive.
More non-native accented speech data is necessary to enhance the performance of … existing [speech recognition] models. However, its synthesis is still an open problem.
Based on the received examples, the team created a phonetic data set, through which a neural network, often used for speech recognition, was trained. The accuracy of the definition of words, after overcoming the mark of 800,000 examples, was 59%.
The study was called preliminary due to fewer sounds in the Carnegie-Mellon University dictionary. Despite phonetic coincidences in 13 out of 20 dictionary comparisons, scientists managed to increase the data array from 103 thousand phonetic transcriptions with one accent to 1 million samples with several accents.