Many organizations are waiting patiently for any signs of progress when it comes to machine translation, and they may have just got what they’re looking for from Google. Google Brain’s new take on machine translation translates speech directly into text into a language that’s needed.
Usually, in this kind of program translates speech into text first then into text in a different language, but Google Brain took the more direct route. “By skipping transcription, the approach could potentially allow for more accurate and quicker translations,” said the team behind it. These researchers also authored a paper entitled “Sequence-to-Sequence Models Can Directly Transcribe Foreign Speech” in which they describe their approach in more detail.
“We present a model that directly translates speech into text in a different language. One of its striking characteristics is that its architecture is essentially the same as that of an attention-based ASR neural system,” explains the authors. For testing, the team trained the system using hundreds of hours of Spanish audio linked with corresponding English text. They then analyzed the Spanish audio’s waveform to see which spoken parts corresponded with which written parts.
The results of the study are promising and show just how good we can get these things with a little work. Using the BLEU (Bilingual Evaluation Understudy) score as a reference, the model performed far better than any other machine out there currently. But, this is just the beginning for the team. Moving forward they would like to continue with their research and in their paper wrote, “An interesting extension would be to construct a multilingual speech translation system following in which a single decoder is shared across multiple languages, passing a discrete input token into the network to select the desired output language.”
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