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Who Stays Behind Your New Voice Assistant, or What I Learned as a Language Annotator for Big Tech

Have you ever caught yourself wondering what is behind the new “Captions” feature on Instagram, or how your bright new Alexa understands you right off the bat?

By Natalia KuzminykhPublished 2 years ago 3 min read
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Who Stays Behind Your New Voice Assistant, or What I Learned as a Language Annotator for Big Tech
Photo by Ivan Bandura on Unsplash

Echo, Home Pod, Alexa, Cortana, Bixby, Siri — this list has significantly expanded in the last few years, and with the shift to a hybrid model of working, even more technologies are waiting for us in the near future.

But what makes it all possible, and why does it stay in the shadow for an ordinary customer? I am going to share what I learned after working as a Language Annotator for Big Tech companies and how you too can make money on it.

What is language annotation?

The steady growth of algorithms today creates a demand for high-quality data. However, the true challenge comes from data annotation, which is an essential step in building machine learning models and has to be done by human annotators. Typically, it involves transcribing or/and labelling a large amount of raw audio material with subsequent analysis.

Depending on the project, an annotator may be asked to focus on different tasks, such as

  • Speech to Text Transcription, which enhances ML models’ ability to capture various utterances after training. In particular, this method made it possible for TikTok and Instagram users to display speech from short-live videos on the screen;
  • Audio Classification instead focuses on contrasting real voices from sounds and background noise (such as radio, TV or animal sounds). This type of audio annotation is crucial for the development of voice and digital assistants, as it helps them recognize who is performing the voice command;
  • Natural Language Utterance involves the categorization of raw speech (from semantics and context to dialects and intonation) in order to train virtual assistants and chatbots to distinguish the user's intent;
  • Speech Labelling is now widely used by music streaming services, such as Spotify, to match a given recording with its lyrics.

How to become an annotator?

By Soundtrap on Unsplash

Although annotation is vital for developing numerous algorithms, it is highly repetitive and time-consuming. Therefore, Tech giants opt not to involve highly qualified engineers for such tasks, preferring to share data annotation with third companies.

Thus, if you are looking for remote job opportunities in annotation, I would recommend first to pay attention to:

  • Telus International AI , the company's main objective lies in providing high-quality data for various business inquires. Therefore, it is a perfect place if you just start in language annotation, as you can find job announcements for many languages (starting from English to Vietnamese). It is also willing to psychological support its freelance workers;
  • Lionbridge was not so much time ago a part of Telus AI, but now it is developed in a separate company, with quite mixed services: translation, localization, annotation, and even game testing. Importantly, Lionbridge prioritize workers with certain experience and hence would be a good place if you are looking for an opportunity to grow as a specialist.
  • Appen, in contrast to the above-mentioned platforms, focuses on the growing Chinese market and can be suitable for candidates speaking Asiatic languages. Sometimes it also opens the possibilities to contribute to a one-time task among projects that seek only experts;
  • Neevo presents an artificial intelligence community or platform that equally distributes simple language tasks between its annotators, on average one can make 20 USD per task here;
  • RWS Moravia Group is a big company that is working on translation and localization of various products and services. It usually requires extensive experience for open positions, but recently it started offering freelance opportunities for new grads.

Did you enjoy reading this article? Consider subscribing to my feed to stay tuned for the next posts. You also can support my sleepless nights when I am creating the content by buying me a coffee.

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About the Creator

Natalia Kuzminykh

As a linguist, I highly value the opportunity to adjust empirical research for different business inquiries, and to improve language-related technologies. Feel free to send me a message to connect further!

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