"Machine translation (MT) is the application of computers to the task of translating texts from one natural language to another. One of the very earliest pursuits in computer science, MT has proved to be an elusive goal, but today a number of systems are available which produce output which, if not perfect, is of sufficient quality to be useful in a number of specific domains."
– Definition from the European Association for Machine Translation (EAMT).
The new system, dubbed the Translatotron, has three components, all of which look at the speaker's audio spectrogram--a visual snapshot of the frequencies used when the sound is playing, often called a voiceprint. The first component uses a neural network trained to map the audio spectrogram in the input language to the audio spectrogram in the output language. The second converts the spectrogram into an audio wave that can be played.
Google AI yesterday released its latest research result in speech-to-speech translation, the futuristic-sounding "Translatotron." Billed as the world's first end-to-end speech-to-speech translation model, Translatotron promises the potential for real-time cross-linguistic conversations with low latency and high accuracy. Humans have always dreamed of a voice-based device that could enable them to simply leap over language barriers. While advances in deep learning have contributed to highly improved accuracy in speech recognition and machine translation, smooth conversations between different language speakers remained hampered by unnatural pauses during machine processing. Google's wireless headphone Pixel Bud released in 2017 boasted real-time speech translation, but users found the practical experience less then satisfying.
On Wednesday, Google unveiled Translatotron, an in-development speech-to-speech translation system. It's not the first system to translate speech from one language to another, but Google designed Translatotron to do something other systems can't: retain the original speaker's voice in the translated audio. In other words, the tech could make it sound like you're speaking a language you don't know -- a remarkable step forward on the path to breaking down the global language barrier. According to Google's AI blog, most speech-to-speech translation systems follow a three-step process. First they transcribe the speech.
Google Translate is one of the company's most used products. It helps people translate one language to another through typing, taking pics of text, and using speech-to-text technology. Now, the company's launching a new project called Translatotron, which will offer direct speech-to-speech translations – without even using any text. In a post on Google's AI blog, the team behind the tool explained that instead of using speech-to-text and then text-to-speech to convert voice, it relied on a new model (which runs on a neural network) to develop the new system. Get 50% off tickets if you buy now.
Do you dream of harnessing your engineering and machine learning knowledge to enrich users' lives? To improve their privacy and security? To expand their ecosystems by opening up models and data to the world? If so, you should join Mozilla's Machine Learning group as part of the three year, EU funded Bergamot Project. The goal of Bergamot is to extend Firefox with an open, on-device neural machine translation (NMT) engine, making translation local, private, and secure.
Scientists are constantly figuring out how to expand the field of use of this incredible invention, which enables computer software to progressively improve its actions by adopting knowledge gained from previous experience. Machine learning, also referred to as artificial intelligence due to its ability to perform tasks using its own judgment, has been the subject of both praise and controversy. However, the sophisticated algorithms that have served in providing you ads on social networks might have a grand future in philology, archaeology, and linguistics. According to Émilie Pagé-Perron, a Ph.D. candidate in Assyriology at the University of Toronto, we might be closer than we thought to deciphering numerous Middle-Eastern cuneiform tablets written in Sumerian and Akkadian languages, all of which are several thousand years old. Pagé-Perron is in charge of the project officially titled Machine Translation and Automated Analysis of Cuneiform Languages, which currently operates in Frankfurt, Toronto, and Los Angeles, using combined efforts to create a program capable of translating the clay tablets.
This work presents a joint solution to two challenging tasks: text generation from data and open information extraction. We propose to model both tasks as sequence-to-sequence translation problems and thus construct a joint neural model for both. Our experiments on knowledge graphs from Visual Genome, i.e., structured image analyses, shows promising results compared to strong baselines. Building on recent work on unsupervised machine translation, we report the first results - to the best of our knowledge - on fully unsupervised text generation from structured data.
Most machine translation systems generate text autoregressively, by sequentially predicting tokens from left to right. We, instead, use a masked language modeling objective to train a model to predict any subset of the target words, conditioned on both the input text and a partially masked target translation. This approach allows for efficient iterative decoding, where we first predict all of the target words non-autoregressively, and then repeatedly mask out and regenerate the subset of words that the model is least confident about. By applying this strategy for a constant number of iterations, our model improves state-of-the-art performance levels for constant-time translation models by over 3 BLEU on average. It is also able to reach 92-95% of the performance of a typical left-to-right transformer model, while decoding significantly faster.