Otter.ai announced its largest language expansion on November 10, adding 27 languages to its AI meeting and transcription platform. Thirteen languages are available immediately, while another 14 are planned for a later rollout. The company also announced multilingual transcription designed to follow a conversation when participants switch between supported languages during the same meeting. The feature is intended for international teams that move between languages in sales calls, project reviews and internal discussions. Otter says the update will allow users to keep a continuous transcript without manually changing the language setting each time the conversation shifts.
The expansion adds regional languages and mixed-language meeting support
The languages available at launch include Mandarin Chinese, Korean, German, Italian, Cantonese, Portuguese, Russian, Indonesian, Malay, Dutch, Turkish, Vietnamese and Polish. The planned additions include Swedish, Danish, Finnish, Arabic, Hindi, Thai, Hebrew, Norwegian, Greek, Macedonian, Persian, Czech, Hungarian and Romanian. Otter says that once the expansion is complete, the service will support 31 languages in total, including the four languages it supported previously. The staged rollout means users should check current availability rather than assume every listed language can already be selected in the product.
The multilingual mode can recognise a language change automatically and continue transcribing. In a meeting that moves from English to Korean and then Portuguese, for example, the system aims to preserve the sequence without asking a participant to stop and change settings. That capability is more demanding than supporting separate single-language recordings. The system must identify the language at the right point, handle overlapping speech and maintain speaker attribution. It also has to cope with names, technical terms and code-switching, where speakers mix words from multiple languages in the same sentence.
Speech accuracy will determine whether the update works for global teams
Otter says the new language models are trained and operated on its own infrastructure rather than being wrapped around a third-party speech service. The company says it used publicly available datasets and native-speaking volunteers to review transcripts and assess quality. Those steps are relevant because speech recognition errors can be uneven across accents, dialects and speaking conditions. A model may perform well on prepared recordings yet struggle with noisy conference rooms, remote microphones or fast conversations. Organisations should test the system with their own speakers and vocabulary before treating transcripts as a reliable record of decisions.
The update extends Otter’s strategy beyond simple transcription into a broader workplace knowledge product. the company introduced features that use meeting history and connected applications to prepare users for upcoming calls, surface previous action items and provide real-time suggestions. Multilingual coverage can expand the addressable market, but it also increases the importance of privacy controls, retention policies and permissions for recorded meetings. Employers should make sure participants know when transcription is active and understand where recordings are stored. The announcement is a meaningful product expansion, while actual usefulness will depend on recognition quality across the languages and environments customers use.
For organisations, the quality of a multilingual transcript should be tested before it becomes the official record of a meeting. A wrong translation of a number, a project name or a commitment can create downstream errors in customer support, legal review or financial reporting. Teams should confirm whether speakers can correct transcripts, whether audio is retained and who can access the recording. They should also check how the system handles overlapping speech and language-specific punctuation. Otter’s own-model approach gives it more control over training and deployment, but it does not remove the need for quality assurance. A language count is a useful measure of coverage; accuracy on real meetings is the measure that matters to users.
Otter’s expansion may also improve accessibility for employees who are more comfortable speaking a language other than English. That benefit depends on whether the service handles regional vocabulary and accents accurately, and whether the interface allows participants to correct mistakes. Language support should be evaluated by real user groups, not only by a list of supported languages.