Human Language Technologies

Human Language Technologies

Human Language Technologies (HLT) comprise a number of areas of research and development that focus on the use of technology to facilitate communication in a multilingual information society. Human language technologies are areas of activity in departments of the European Commission that were formerly grouped under the heading Language Engineering (Gupta & Schulze 2011: Section 1.1).^[72]
The parts of HLT that is of greatest interest to the language teacher is Natural Language Processing (NLP), especially parsing, as well as the areas of speech synthesis and speech recognition.
Speech synthesis has improved immeasurably in recent years. It is often used in electronic dictionaries to enable learners to find out how words are pronounced. At word level, speech synthesis is quite effective, the artificial voice often closely resembling a human voice. At phrase level and sentence level, however, there are often problems of intonation, resulting in speech production that sounds unnatural even though it may be intelligible. Speech synthesis as embodied in Text To Speech (TTS) applications is invaluable as a tool for unsighted or partially sighted people. Gupta & Schulze (2010: Section 4.1) list several examples of speech synthesis applications.^[72]
Speech recognition is less advanced than speech synthesis. It has been used in a number of CALL programs, in which it is usually described as Automatic Speech Recognition (ASR). ASR is not easy to implement. Ehsani & Knodt (1998) summarise the core problem as follows:
"Complex cognitive processes account for the human ability to associate acoustic signals with meanings and intentions. For a computer, on the other hand, speech is essentially a series of digital values. However, despite these differences, the core problem of speech recognition is the same for both humans and machines: namely, of finding the best match between a given speech sound and its corresponding word string. Automatic speech recognition technology attempts to simulate and optimize this process computationally."^[73]
Programs embodying ASR normally provide a native speaker model that the learner is requested to imitate, but the matching process is not 100% reliable and may result in a learner's perfectly intelligible attempt to pronounce a word or phrase being rejected (Davies 2010: Section 3.4.6 and Section 3.4.7).^[40]