Of
course,
translation is much more
complex, so these machine learning tech- niques include processing large volumes of highly structured, annotated language data to develop models that recognize the relationships among the elements of speech. For the speech app, these language components are the automated speech recognizer, the machine- translation engine and the text-to-speech speech synthesizer.
A microphone captures speech, and the automated speech recognizer turns it into text data by using a probabilistic model that finds the most likely match between the speech that’s been converted to text and what the machine has learned. After the speech is converted to text, the text shows on the display so the user can decide whether it is correct. Tis is how the Google Assistant, Apple’s Siri and others “understand” you when you ask them to find the next nearest gas station, or when you ask Amazon’s Alexa to play a specific song from your music library.
Te automated speech recognizer doesn’t complete all of the requested translation task; its job is done when it passes the recognized speech in text to the next pro- cess, machine translation.
A SIMPLE SETUP
MFLTS’ apps are available for the smartphone- like Nett Warrior device and for download to use on a laptop, as in this configuration, which also includes a scanner to input documents for text translation and a microphone for speech translation. (Photos by Tracy Blocker, MFLTS Product Office)
CORE PROCESSES Inside a language pack, the machine- translation engine is the component that performs the “magic” of the actual translation. Like the automated speech recognizer, a machine-translation engine uses probabilistic models that are trained using dual-language sets of data devel- oped with the expertise of people fluent in both languages in a language pair— for example, English and Arabic.
Not surprisingly, developing machine- translation engines for unusual pairs of language is often very labor-intensive and expensive because of the scarcity of data and linguists proficient in both lan- guages. As developers of the automated speech recognizer’s model have done, engineers and scientists who are creat- ing machine-translation models rely on techniques for model training that are a combination of science and art.
Machine translation probabilistic models find the best match between the source and target languages and, like the auto- mated speech recognizer,
text output in the target language to a speech synthesizer and to a display, using the target language’s character set.
Finally, for the speech app, the text- to-speech language part of the app is a synthesis program that produces audi- ble speech in the target language. Like the automated speech recognizer and machine translation, the text-to-speech component relies on extensively trained speech-synthesizing models that provide the text-to-speech conversion.
After receiving the text from the machine-translation engine, the text-to- speech function converts
the text
into
spoken language by putting together words or phrases from recorded speech of the target language. MFLTS then plays the text-to-speech content on the inter- nal or external speaker of, typically, a smartphone.
Working together, these language com- ponents
are the
appearing to hear, understand and trans- late English into another language.
then provide
Let’s say that a Soldier has just translated speech from an Arabic-speaking local. Now the Soldier needs to reverse the pro- cess, translating from English to Arabic. Tat’s no problem; it’s why the MFLTS language packs always travel in pairs.
“brain” of MFLTS,
ASC.ARMY.MIL
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SCIENCE & TECHNOLOGY
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