Cypriot Greek speakers may soon be understood by the same voice-activated systems that routinely struggle with the island’s dialect, thanks to a breakthrough speech-to-text AI model developed by a three-person team.
Igor Akimov, an AI product manager at a foreign-interest company, has joined forces with two interns – Hussein Khadra and Nikita Markov, students at the University of Nicosia and UCLan – to tackle the problem of local dialects not being properly recognised by the technology that many people depend on.
The team has created a speech-to-text AI model, an automatic speech recognition system fine-tuned to accurately understand and transcribe Cypriot Greek. Users speak into a microphone, and the system converts speech to written text, a technology which can then be applied to AI voice agents, translation services or automated phone support.
The technology also has broader applications across multiple sectors. In healthcare, it can automatically transcribe patients’ speech, particularly that of older adults, and input it directly into medical systems without manual typing. In business, it enables automated voice agents that can interact naturally with Cypriot customers, while in education, it can help preserve the Cypriot dialect and culture by digitising the island’s audio archives.
The same approach could be applied to other overlooked languages and dialects. One of the team’s main goals was to understand how to work with languages that lack sufficient data, a methodology they believe could be replicated globally.
“It was not easy. I think we all underestimated just how complex it would be. There were definitely ups and downs along the way,” Akimov admits.
When looking for data resources, the team reached out to researchers but found little help. Responses ranged from data being lost, to requests for high fees, or outright refusal.
They scoured dictionaries, texts and audio samples, but could not find high-quality, accessible datasets that paired speech with transcribed and validated text.
Even Meta, which has collected data for 1,600 languages, had zero hours of Cypriot speech available.
“So, we had nothing to start with, therefore, we decided to gather all the available Cypriot audio from TV shows to radio stations, podcasts and books,” said Akimov. “Step by step, we created the largest Cypriot Greek speech collection ever assembled.”
Training the AI was a gradual process. In the first phase, the system absorbed everyday Cypriot Greek speech, its sounds, rhythms and unique traits, to get a sense of how the dialect naturally sounds.
Next, the team fed it clearer, professional speech from news broadcasts and radio shows, helping the AI refine its understanding and reduce errors. A special reading assistant, KenLM, was also added to act almost like a tutor, suggesting the most likely words and boosting recognition accuracy.
To keep the model improving, the team built a platform where native speakers can correct the AI’s transcripts. These corrections are fed back into training, making the system increasingly accurate and faithful to the Cypriot dialect over time.
Remarkably, all of this was accomplished on a budget of just $150, thanks to creative approaches and accessible cloud technology.
Yet, the project is far from finished. “With only a few hours of high-quality transcribed audio, we couldn’t create the world’s best model yet – but it’s absolutely achievable,” Akimov explained. “Right now, it’s more of a technological proof-of-concept waiting for more data.”
So far, the team has collected about 300 hours of Cypriot speech and is seeking help from volunteers. Spending just 15 minutes validating transcriptions on the project website could provide enough data to build a state-of-the-art model for Cypriot speech recognition, and potentially even a text-to-speech system that speaks in authentic Cypriot Greek.
Interested individuals can visit voiceofcyprus.org to validate audio recordings.
“This will help us – and Cyprus – tremendously. Even just 10-15 minutes makes a difference,” Akimov said. “We want every Cypriot to be able to speak in their own dialect and still be understood by technology.”
