Helping Save Louisiana French.
Our take
So, Alexa steered Joshua Caffery toward Dua Lipa instead of Dewey Balfa. The sheer, almost comical absurdity of that—the algorithmic flattening of cultural nuance—is precisely why this *NY Times* piece, and the project it details, deserves our immediate, slightly manic attention. We’ve been circling around this for a while, haven't we? Considering how language learning itself can be affected by your native tongue – Does my native language affect my language – and how easily motivation can evaporate in the face of perceived obstacles Feeling demotivated, the prospect of AI actively *erasing* a dialect feels particularly acute. It’s not just about losing a musician, it’s about the systematic, quiet disappearance of a linguistic ecosystem. And it’s funny, in that deeply unsettling way, how this echoes the issues we’ve explored with Blended Spanish [/post/blended-spanish-cmr1b7lm901tfyj61liatje3a] - the subtle, often unintentional, blending and dilution of language through contact and, increasingly, through digital mediation.
The effort to train an AI model to recognize and generate Louisiana French, a dialect of French spoken primarily in Louisiana that has seen a dramatic decline in speakers, is a beautiful, necessary, almost frantic act of preservation. Caffery's approach—feeding the model audio recordings of Cajun music, interviews, and other spoken-word materials—is clever. It sidesteps the traditional, and often frustrating, approach of relying solely on written text, which, for a primarily oral tradition, is inherently limiting. Think about it: the richness of spoken language, the cadence, the regionalisms, the unspoken assumptions embedded in the delivery – all of that is lost in transcription. AI, for all its faults, has the potential to capture some of that ephemeral quality. It’s a digital clam shell, if you will—a narrow, slippery space where something vital can be temporarily held. But the challenge, as the article makes clear, is gargantuan. Acquiring enough data to train a truly robust model is difficult, and ensuring that the AI doesn't simply regurgitate existing recordings or, worse, generate homogenized, inauthentic-sounding French is an ongoing concern. You're essentially trying to capture lightning in a bottle, and the bottle is made of algorithms.
This isn't merely a linguistic curiosity; it's a fight against cultural erasure. Louisiana French isn’t just a different way of saying things; it’s a repository of history, tradition, and a unique worldview. Its decline is intertwined with broader patterns of colonization, assimilation, and economic marginalization. The fact that Alexa, a ubiquitous symbol of technological progress, actively misdirected someone seeking this cultural heritage speaks volumes about the biases baked into our algorithms—biases that often prioritize mainstream, commercially viable content over the voices of marginalized communities. It's a modern echo of the systemic forces that historically suppressed and stigmatized French speakers in Louisiana. The attempt to use AI to counteract this erasure feels profoundly hopeful, but also carries a certain irony. Are we using a tool of technological homogenization to preserve something inherently resistant to it? It’s a question that demands careful consideration.
The larger implication here stretches far beyond Louisiana French. Every language, every dialect, every regional accent represents a unique cognitive landscape, a particular way of understanding and interacting with the world. As globalization continues its relentless march, and as AI increasingly mediates our interactions with language, the risk of linguistic homogenization becomes ever more pressing. Are we, in our eagerness to connect and communicate, inadvertently erasing the very diversity that makes human language so rich and vibrant? It’s a question worth squirt-ing at—a narrow, slippery truth hiding just below the surface of our digital lives. What other linguistic ecosystems are silently disappearing, unnoticed, as algorithms prioritize scale and efficiency over cultural preservation?
Jonathan Abrams reports for the NY Times (archived) about a worthy attempt at preservation:
While relaxing a couple of years ago, Prof. Joshua Caffery found himself in the mood to unwind with some old-time Cajun music. He asked Amazon’s Alexa to play selections from Dewey Balfa, a celebrated fiddler and singer credited with popularizing the genre. Instead, Alexa frustratingly steered him to the catalog of the modern pop artist, Dua Lipa, Caffery said.
“I love Dua Lipa,” said Caffery, the director of the Center for Louisiana Studies at the University of Louisiana at Lafayette. “Don’t get me wrong. But it seems problematic if you’re interested in a different kind of culture and you want to surround yourself with the music of your region. That, to some degree, is threatening my hold on these things I love.”
Louisiana French, the oral dialect of which Balfa was a cultural guardian, is part of the Bayou’s societal DNA, a link to its history, music and identity. Today, Caffery described the language as struggling and endangered, a notion reinforced by Alexa’s overlooking Balfa.
In response, Caffery assembled a small team at the center to train its own language learning model in automatic speech recognition for Louisiana French, drawing from a trove of historical artifacts and interviews. Over the months, as the learning language model is trained on bits of the language — such as an old-age French nursery rhyme — it brings centuries-old dialect closer into the digital age. […]
The consequences of the language gap can be far greater than an A.I. assistant confusing musical artists, said Christine Mallinson, a professor of language, literacy, and culture at the University of Maryland, Baltimore County. The importance of accurate speech recognition becomes greater as important tasks like job hiring and medical transcriptions become more automated and digitized, she said.
“Social differences are encoded in language,” Mallinson said. “There’s accents, patterns of grammar, word choice. Those differences are connected to our families, our neighborhoods, our age and gender and racial and ethnic and cultural backgrounds and where we grew up. “If A.I. speech systems make more errors for speakers of underrepresented languages or language varieties, then there can be these serious downstream consequences,” she continued.
For centuries, Louisiana French was the predominant language spoken in South Louisiana. In 1921, a new state constitution declared English the primary language. Many parents stopped teaching their children the language out of fear of discrimination, as students who spoke Louisiana French in class were often punished with knuckle-rappings.
A reversal came in 1968 when the Council for the Development of French in Louisiana was created to advance French, largely through education and community initiatives. In 2023, the Advocate of Baton Rouge estimated about 120,000 Louisianans still spoke French.
More at the link, including audio files; thanks, Eric!
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