Modern technology, especially the technology of the last five years, is something simply incredible. From the moment the first AI models became generally available, people started predicting a great and sometimes even frightening future for these technologies. At first, they were just language models that could hold a conversation and find information. Then neural networks began to appear that could already draw images, and even later the first neural networks capable of creating music appeared.
Now the world is enjoying neural networks that can even create videos, as well as translate, voice, and even adjust lip movements. We will tell you how these innovative technologies will soon be used for educational purposes.
Where It All Began
A couple of years ago, the Internet community was amazed by the appearance of the first neural network capable of generating video sequences. Videos depicting nature looked great even then, but it was different with people. Neural networks often heavily distort people’s faces emotional expressions and bodies, and the actions in such videos cause many different emotions: from incomprehension to laughter and even shock. But time was running out.
A year later, such neural networks made a significant step forward. Although the situation with the depiction of people has improved, still neural networks have made great progress and it was possible to watch movie scenes in these videos with interest rather than disgust. Now neural networks generating video sequences have practically learned to depict people and animals. The number of situations when a person suddenly grows a third arm from somewhere has significantly decreased.
But despite all the nuances that video neural networks have in them, their tremendous potential never could be utilized even in the era of their very first versions. That’s when many developers started trying to combine the work of several different AIs into a single entity.
The idea is to develop an AI model capable of translating, voicing and even imitating the lip movements of spoken words by a person who speaks another language. And the whole point is that the AI not only translates and voices the translated text but also copies the original human voice!
Machine Learning in Education
All neural networks are built with machine learning. This technology is used to suit learning materials to a particular student’s needs as well as automate the processes of translation and audio content creation.
Using a translation tool on the Rask AI video translator, teachers and educators can make videos translated into other languages so children can more easily understand complex subjects like science experiments.
Here are the specific benefits for students and teachers:
For students:
Adapting materials to students whose first language is not English becomes critical and provides AI tools that can generate quality translations of learning materials while keeping the meaning and structure of the original educational content together.
It’s particularly true for scientific disciplines where high accuracy of and accessibility of information are important to understanding complex subjects.
For teachers:
AI-powered tools help teachers create course materials that can be translated into multiple languages, keeping them accessible to all students.
Innovations in the digital age like virtual reality, ai lip sync, and automatic translation add to the excitement of the learning activity and provide a means for more students to be successful.
Overcoming Language Barriers
Our world is very diverse and so it has its pros, challenges, and cons. For example, one of the major barriers international schools face is to modify teaching materials for those students who speak different languages.
This, in turn, creates many other challenges associated with accurate interpretation and adapting terms and concepts to other languages. But that’s where AI tools for video translation and synchronization come to the rescue.
Here are some specific examples where language features have become a problem in science:
#1: Chemistry and Hess’s law
In a chemistry class, the teacher teaches the students Hess’s law which is one of the main thermodynamics principles. The main problem the word “force” is polysemous, that is to say, it can have different meanings depending on the language.
Force in English, means physical impact, but in French, we have the same word “force” which can also mean “intensity” or “effort”. This can lead students to be a little confused.
In the French version, AI will select the term ‘puissance’, choosing not to fall back on “force” which could be misleading, when it is in the context of strength.
#2: Physics and Newton’s law
One classic example to describe language barriers is how Newton’s second law is different in different languages. In English formulation is: Force equals mass times acceleration. But in Spanish masa (mass) can be easily confused with materia (matter), so a mistake is made between the mass and the matter.
#3: Biology and the term “inheritance”
Many teachers in biology, teaching genetics, face the problem of replacing the word “inheritance” (“interested” and “interest” will not do as a translation). In English, it’s as clear as transparency could be that genes are transmitted from parents to offspring. But in Chinese, the word “继承” (jìchéng) can simply be the property inheritance, as opposed to genetic information. It creates confusion for most students.
#4: Math and the term “limit”
A function’s limit is a term used in Math, which in English is called “limit”. In some languages, such as German and French, this word has different meanings. For example, in German the word “limit” means the meaning financial limit rather than mathematical limit. This may lead students to misrepresent problems involving the calculus of limits.
The word ”limit” will be properly translated by AI and it will replace “limit” with “Grenzwert” for the German version and “limite”, for the French version.
#5: Chemistry and the term “alkali”
The word “alkali” in chemistry can be a difficult one to translate. In Arabic, as in other languages, the word “alkali” is similar in sound to another word for salt, or base. As a result, learners can confuse alkali with other substances.
AI will pick the correct word for alkalis for an Arabic audience “قواعد” (qawa’id).
Is Such AI a Bad for English Language Learners?
Some believe that the proliferation of such AIs will ultimately make learning English skills obsolete. But we don’t agree with that. The neural networks simply operate only within the video. You can’t replace knowing languages to communicate in real-world scenarios of life.
AI models that can do such operations in real-time are a distant future, as they need power. Moreover, we argue that even if such AIs can work in real-time, they will always be going to be lacking and people will still have to learn foreign languages. And here’s why:
- If you’re going to use these technologies for real-time communication you now need additional devices that will analyze your voice and output translated text through some sort of speaker. To be blunt, this is strange;
- Because you need to hear the speech being translated by the AI, you’ll always have to wear headphones. There will at least still be some delay;
- There are words inside languages of the world, aphorisms, and already established verbal turns of phrase that literally have no analogs in other languages. This means that without the knowledge of the language, you will still not get much.
So you can’t worry that such AIs will eliminate the need to know languages in the near future. The only undeniable fact is that these will become more popular each day.
How AI Lip Sync Tools Work
The Rask AI Platform is a complex architecture that includes several key components:
#1. The translation and voicing architecture
The latest modern machine learning, translation, and dubbing system is built around Transformer Neural Network architectures. Due to the efficiency in processing sequential data, it has replaced models of the earlier times, including; recurrent neural networks (RNN) LSTM, etc.
The architecture work scheme:
- Encoder and decoder: Input text or audio in the source language is fed to the encoder, which converts it into so-called hidden representations (vectors). they’re used to generate text or audio (whichever it may be) in the target language by decoder;
- Training on large datasets: Typical training of neural networks relies on large datasets of paired texts, the original text, and the translation, so they can more confidently learn about the linguistic and grammatical features of one language versus another. These datasets also contain different dialects, speech styles, and accents, which translate things more accurately;
- Speech generation: The text-to-speech (TTS) process is the one that voicing the text. This is done using text-to-speech models such as Tacotron 2 or WaveNet. The produced speech from these models is natural, with intonation and tone adjusted to context.
#2. Lip sync and facial motion
Various methods and neural network architectures are used to generate a translated video, with voiceover in another language, which adjusts the visual elements to the voiceover.
The working principle of motion synthesis:
- Recognizing key points on the face: In order to correctly synchronize lip movements with speech, the neural network must first recognize the main key points on the face (e.g., corners of the lips, jaw, eyes). This is done using deep neural networks trained on video, which track the position of different parts of the face depending on the sounds being spoken;
- Correspondence between sound and lip movements: For each phonation gesture (phoneme), there is a specific lip movement pattern. A lip sync neural network is trained on thousands of hours of video to learn to predict how the lips should move when saying certain sounds. This process involves the use of phono-facial maps, where a specific lip movement corresponds to each phoneme;
- Facial motion generation model: In addition to lips, such systems also control the movement of other parts of the face to make expressions natural. GAN (Generative Adversarial Networks) based techniques are used which help in generating realistic images of faces and their movements based on audio data.
Conclusion
Using the Rask AI Platform to translate, voice and lip-synchronize movements opens up huge opportunities in the educational sphere. This technology helps teachers easily customize learning materials for students from different countries, making learning accessible regardless of language barrier. Teachers can provide a deeper understanding of complex topics, such as science experiments, and increase student engagement in learning experiences.
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