NETtalk (artificial neural network)

NETtalk is an <a href="/facts/Artificial_neural_network/6V1jMlkx">artificial neural network</a> that learns to pronounce written English text by being shown text as input and matching <a href="/facts/Phonetic_transcription/bfbFHWur">phonetic transcriptions</a> for comparison.
It is the result of research carried out in the mid-1980s by <a href="/facts/Terrence_Sejnowski/rckEWrwp">Terrence Sejnowski</a> and Charles Rosenberg. The intent behind NETtalk was to construct simplified models that might shed light on the complexity of learning human level <a href="/facts/Cognitive/ga0tSBul">cognitive</a> tasks, and their implementation as a <a href="/facts/Connectionist/P8It9YaE">connectionist</a> model that could also learn to perform a comparable task. The authors trained it by <a href="/facts/Backpropagation/lCsIdKHc">backpropagation</a>.
The network was trained on a large amount of English words and their corresponding pronunciations, and is able to generate pronunciations for unseen words with a high level of accuracy. The success of the NETtalk network inspired further research in the field of pronunciation generation and speech synthesis and demonstrated the potential of neural networks for solving complex <a href="/facts/Natural_language_processing/1hjMKsSN">NLP</a> problems. The output of the network was a stream of phonemes, which fed into <a href="/facts/DECtalk/3Iz0PjEN">DECtalk</a> to produce audible speech, It achieved popular success, appearing on the <a href="/facts/Today_(American_TV_program)/WRY0PJgB">Today show</a>.: 115

NETtalk (artificial neural network) open-in-new

NETtalk (artificial neural network)