white paper
sketch
Native Voices YouTube feature
Scope – Cover pre-1974 literature, prioritizing Western Civ English classics (e.g., Shakespeare, Austen) via native English speakers. Extend to global works like Sanskrit epics or Latin texts in original languages.
Filters & Verification – Use AI to check accents/pronunciation; require creator self-verification or community flags for authenticity.
Absolutely—AI’s strength in pattern recognition makes it ideal for accent verification without human bias. We could integrate models like those in speech tech (e.g., similar to Duolingo’s checks). Add ethical guidelines to the white paper? What else to include?
Expansion – Tiered access—basic for casual users, premium for educators with annotations.
Great point—tiered access could indeed self-fund via subscriptions or district licensing. Imagine premium tiers with customizable annotations for curricula, integrated quizzes, or even VR immersion for classics. How about adding a community contribution model for annotations? What else to include?
Challenges & Solutions – Address biases via diverse moderation; integrate with existing language tools.
Challenges: Moderation biases could favor certain dialects (e.g., favoring British over American English for classics). Integration might clash with tools like auto-subtitles that mispronounce words (e.g., “quixotic” as “quick-sot-ick”).
Solutions: Diverse moderation panels including global linguists for fair verification. Integrate with tools like Google’s Cloud Speech-to-Text for accent detection, or YouTube’s caption AI for real-time pronunciation flags, ensuring cross-cultural accuracy.
Agreed—AI managing mechanics (quizzes, drills) scales classes efficiently, letting teachers dive into interpretive depths like symbolism or ethics in lit. This boosts accessibility without diluting quality. White paper addition: “Scalable Class Models” with AI feedback loops. Thoughts on integrating real-time AI tutoring in group settings?
PEACH COMMUNITY 