Upload README.md with huggingface_hub

README.md

@@ -131,10 +131,10 @@ To re-extract embeddings from the audio (~24 CPU-hours plus ~1 GPU-hour for Whis
 
 ## Source data and collection
 
-- **Speakers.** 100 English-speaking US celebrities stratified across 5 sociophonetic groups (Italian-American, African-American, Asian-American, Latinx, mainstream Standard American English) x 2 genders x 2 age brackets, 5 speakers per cell.
+- **Speakers.** 100 English-speaking US celebrities stratified across 5 sociophonetic groups (1 = New York City English, 2 = Southern American English, 3 = African American English, 4 = Latino English, 5 = Asian American English) x 2 genders x 2 age brackets (1 = under 45, 2 = 55 or older), 5 speakers per cell.
 - **Reference audio.** Clips selected from publicly available recordings (interviews, podcasts).
-- **Voice clones.** Generated with a state-of-the-art TTS system (Cartesia Sonic) from a short reference clip per speaker.
-- **Voice morphs.** Continuously morphed pairs sweeping a voice-conversion latent between two source speakers, sampled at 11 levels per pair.
+- **Voice clones.** Generated with Cartesia (a state-of-the-art TTS system) seeded from a natural source clip of the speaker being cloned. The variant letter in the stimulus ID identifies the seed: a Type 3 clone shares its seed clip with the comparison clip of the matched Type 2 pair, and a Type 5 clone shares its seed with the matched Type 4 pair (e.g., the clone in `3_F01B` is seeded from the same F01B source clip used as the comparison in `2_F01B`).
+- **Voice morphs.** For each of the 100 reference speakers, the latent voice representation of the reference speaker is interpolated toward each of 4 within-group comparison speakers (matched on sociophonetic group, age group, and gender), at 2 distinct recordings per comparison speaker, sampled at 10 morph scales between 0 and 1, plus 1 anchor at scale 1. This yields 4 x 2 x 10 + 1 = 81 Type-6 stimuli per reference speaker (8,100 total). Generated using the voice-morphing feature of the same Cartesia TTS system.
 - **Listeners.** 1,290 adult English-speaking participants recruited via the Centaur AI platform under an IRB-approved protocol. Consent followed the platform's standard pipeline.
 
 Each pair received at least 8 judgments; real-speech pairs (Types 1, 2, 4) received more coverage than synthetic pairs to give tighter consensus estimates on the real-speech reference distribution.