Google preparing to launch simultaneous translation service
Google was preparing to bolt two existing systems together and, in doing so, attempt something the language industry had been promising and failing to deliver for fifty years: translation of speech as it is spoken, on a phone, for anyone.
Mustafa Icil, Google's marketing manager for Turkey, the Middle East and Africa, laid out the architecture plainly. "We have two types of software at the moment, one is for turning the sounds into written text that we use in voice search and the other one is translating the written text into other languages that we already use in Gtalk," he said. The company was already providing a multilingual written translation service. The remaining step was to connect the two pipes.
How the system learns
Icil said the way the system learned languages and different accents was very similar to the way humans acquire a second language: through exposure, through volume, through hearing a great deal of it. That analogy is a marketing simplification, but it points at something real about the method.
Neither component was hand-built from grammatical rules. Speech recognition improved by being fed enormous quantities of recorded speech with known transcriptions. Statistical translation engines improved by being fed enormous quantities of text that already existed in two languages, much of it from the parliamentary proceedings of multilingual legislatures, which is why early Google Translate output had a faintly bureaucratic register.
Google's structural advantage was never a cleverer algorithm. It was the data. Voice search gave it recordings of ordinary people speaking badly, into bad microphones, in noisy rooms, with accents, which is precisely the material a robust recogniser needs and precisely what laboratory corpora lack.
Where the errors multiply
The engineering problem with chaining two systems is that the errors do not add. They multiply. If the recogniser is 95 per cent accurate and the translator is 90 per cent accurate on clean text, the combined system is not 90 per cent accurate. It is worse, because the translator receives a corrupted input and translates the corruption with total confidence.
A misheard word does not produce a slightly wrong translation. It produces a fluent, grammatical sentence about something else entirely, delivered in a calm synthetic voice. The system has no mechanism for signalling doubt.
What a human interpreter is actually doing
This is where the comparison with professional practice gets uncomfortable for the technology, and it is worth being precise about why.
- Prediction. A simultaneous interpreter is running ahead of the speaker, anticipating where the sentence is going, which is how they keep up with German verb-final structures at all.
- Compression. They discard redundancy, filler and false starts, and deliver meaning rather than words.
- Register. They know when a formulation is diplomatic, insulting, or a joke that will not survive the crossing, and they adjust.
- Repair. They notice when they have gone wrong, and they fix it aloud.
- Context. They read the room, and they know who is in it.
A pipeline of recogniser plus translator plus synthesiser does none of these things. It transcribes, converts and speaks. That is a genuinely useful trick, and it is not interpreting.
The accent problem, and the latency problem
Icil's comparison with human language acquisition glossed over the two failures that shaped the product more than anything else. The first is accent. A recogniser trained mostly on speakers from one region degrades sharply on speakers from another, and the degradation is not random. It falls hardest on exactly the populations least represented in the training data, which for a long stretch meant non-native speakers, older speakers and regional varieties far from the corpus. Speaking to a machine in the language you learned at forty, in a second-language accent, produced dramatically worse results than speaking to it in a clean broadcast accent from a wealthy market.
The second is delay. A conversation collapses if the pause between turns runs past a second or two. Every stage of the pipeline adds latency: capture the audio, transcribe it, translate it, synthesise it, play it back. Early versions were slow enough that people simply stopped waiting and reverted to gestures. Most of the engineering effort since has gone not into making the translation better but into making it faster, because a mediocre answer in half a second is more usable than a good one in five.
The market that opened anyway
None of which stopped the technology from being valuable, because it turned out that the enormous majority of cross-language speech has nothing to do with diplomacy. It is asking directions. It is a taxi. It is a doctor establishing whether the pain is sharp or dull. It is a tourist and a shopkeeper agreeing on a price.
For that traffic, an imperfect machine that is instantly available beats a perfect human who is not available at all. The technology did not take work from conference interpreters. It served a demand that was never going to hire one, and it created a habit of expecting cross-language communication to be free and instant, which is a considerably larger change than any single product.
What it did to the professionals
The effect on working translators was real but not the one predicted. Nobody put a machine in the booth at a summit. What happened instead is that the floor of the market moved. Text that would once have gone to a junior human now goes to a machine, and a human is paid to fix it afterwards, at a lower rate, under a different job title.
Professionals arguing this out on r/TranslationStudies have generally converged on a division that has held up well. Where an error is embarrassing, the machine wins on cost. Where an error is dangerous, expensive or actionable in court, it does not get near the job. Practitioners who learned to fold machine translation into a professional workflow, rather than treating it as a competitor, have generally done fine.
Icil's two systems were duly connected, and speech translation now sits in a billion pockets. It did what he said it would. It just never did what people assumed that meant.