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Deep Language Learning
How technology enhances language instruction.
Technology Is Transforming How Students
Learn a Foreign Language
In Star TrekTM and other science fiction, alien civilizations communicate via a universal translator that seamlessly translates from one tongue to another, mak- ing alien-language instruction superfluous. Unfortunately, such flawless universal translation is unlikely to arrive anytime soon (despite recent advances).
Email:
steven@siliconspeech.com
Steven Greenberg
Address:
Silicon Speech 17270 Greenridge Road Hidden Valley Lake, California 95467 USA
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So, at least for now, the most effective path for communicating in a foreign tongue is through instruction. Traditional language pedagogy emphasizes classroom and laboratory practice of vocabulary, grammar, and pronunciation. Lessons are highly structured, with students practicing language skills in class and laboratory. Feedback is offered mostly through exams and drills. However, this classical approach has se- rious drawbacks, especially when it focuses on declarative knowledge of grammar and vocabulary to the exclusion of conversational skills and comprehension.
Although the ambitious student might achieve conversational fluency by living in a foreign community, this option is unavailable to many. Fortunately, curricula are beginning to incorporate more naturalistic approaches to language learning, powered by technology. The long-term goal is to emulate real-world learning in ways that are effective, economical, and enjoyable.
For computer-assisted language learning (CALL), the “holy grail” is courseware that simulates what a student might experience living in a foreign land. In this virtual community, the student would converse in the target language and receive feedback on ways to improve. Although this pedagogical nirvana won’t happen anytime soon, several advances bring it closer to reality. Among these are
(1) powerful, inexpensive computing residing in the “cloud,” using a multitude (often thousands) of machines (usually graphical processing units [GPUs]) and abundant memory that mobile devices (e.g., smartphone, tablet) and computers can access easily;
(2) large amounts of online data to “train” pattern classifiers known as artificial neural networks (ANNs);
(3) cloud-based “deep learning” neural networks (DNNs). These are especially powerful ANNs that contain many (often dozens of) hidden layers and in- tricate connection topologies. A layer is “hidden” if it lies between the input stage of the ANN and its output (i.e., classifier outcome). Each hidden layer adds a level of processing that facilitates the “learning” (through adjustment of activation weights) of features critical for successful classification;
(4) DNN-trained automatic speech recognition (ASR) and speech synthesis (TTS) that deliver a quality and naturalness close to what humans achieve in many (though not all) languages. Many companies (e.g., Amazon, Apple, Google, and Microsoft) use the technology to interact with customers and clients. The data collected are used to further enhance the technology; and
volume 14, issue 4 | Winter 2018 | Acoustics Today | 19 https://doi.org/10.1121/AT.2018.14.4.21