Creative GenerationReplaces Human LaborVerified
Reviewed and published by trentmaziarz, March 21, 2026. Discovered and drafted by our automated research pipeline.
Every voice learners hear in the Duolingo app — characters speaking sentences, words, and dialogues — is produced by AI voice synthesis, not live human recordings. Duolingo built custom synthetic voices for each of its animated characters by training AI on recordings made by human voice actors. Those voices now generate unlimited audio across 30+ languages automatically.
Details
Duolingo has used text-to-speech (TTS) technology since at least 2017, initially through Amazon Polly. In August 2021, the company announced custom AI voice synthesis built from recordings of auditioned voice actors, using Microsoft Azure Custom Neural Voice technology. The system uses cross-lingual transfer: once a high-quality voice is trained in one language, it can be adapted to additional languages without re-recording. Voice actors provided the original training data but are not involved in producing individual audio clips. All lesson audio is now AI-synthesized at production time.
Products affected
Duolingo
Sources & Evidence
Company Disclosure
Other practices by Duolingo
Customer SvcDuolingo uses an AI chatbot (powered by a company called Decagon) to handle the majority of customer support requests for the Duolingo English Test. The AI fully resolves about 80% of chat inquiries without any human involvement, answering questions about test registration, scores, and institutional acceptance.Data AnalysisDuolingo uses a machine learning model to decide, in real time, whether to show each individual user an advertisement, a subscription offer, or neither — predicting which choice is most likely to generate revenue from that specific person at that moment. The system was credited with generating tens of millions of dollars in additional annual revenue.Data AnalysisDuolingo uses a machine learning algorithm to decide which push notification to send each user, at what time, and how often — optimizing for the messages most likely to get a lapsed learner back into the app. The system was published as research in 2020 and was shown to increase the number of daily active users by 0.5% and improve retention of new users by 2%.
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