Kanien’kéha AI Survey

Kanien’kéha Dialects AI Survey: https://forms.gle/8JKfYogpmXVy1y6j6

Dialects Revival & Retention Projects by MoniGarr.com | MohawkLanguage.ca

Results (anonymized) will be available for Kanien’kéhake, Onkwehonwe, First Nations, Indigenous that request viewing access at MoniGarr.com | MohawkLanguage.ca.

We hope this will help more language revival projects with their proposals and reports.


Kanien’kéha dialects text and pronunciations are getting all mish mashed together into one: creating chaos and confusion regarding spelling and pronunciation.

Kanien’kéha dialects are ’low resource’ (zero resources are dedicated to supporting Kanien’kéha dialects).

Kanien’kéha dialects are also a ’polysynthetic’ language (one Kanien’kéha ’word’ can be an entire sentence / idea).

Kanien’kéha is not the same culture / world view as English, French, Spanish and colonial languages – so putting literal translations between the two can also create chaos, confusion and new problems.

Genocidal policies continue to punish and erase people that choose to continue speaking Kanien’kéha in their own homelands with consequences of loss of jobs & opportunities as well as being erased and cut off from participating in communications, communities and projects that put all resources into supporting English, French, Spanish languages and people.

Zero Kanien’kéha resources are available to the general public for basic communication tasks such as spell checkers, voice user interfaces, phone answering systems, dictation, grammar correction, text to speech, text to text, question answer systems, training systems, etc. The lack of support and resources for Kanien’kéha dialects creates very expensive, labor intensive problems for anyone that is retaining and/or reviving Kanien’kéha dialects – including the Kanien’kéhake (people) in our own communities.

  • Kanien’kéha, Ancient Intelligence, Artificial Intelligence, Machine Language, Neural Language Processing, Lexical Normalization, Part of Speech Tagging, voice user interface, speech engine, deep learning, text to speech, text to text, grammar correction, standardization, linguistics, regression, categorize, speech to text

Categories: AI, ML, NLPTags: , , , , ,


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