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croissant.json

@@ -182,7 +182,7 @@
           "@type": "cr:Field",
           "@id": "speakers/group",
           "name": "group",
-          "description": "Sociophonetic group (1=Italian-American, 2=African-American, 3=Asian-American, 4=Latinx, 5=mainstream Standard American English).",
+          "description": "Sociophonetic group (1=New York City English, 2=Southern American English, 3=African American English, 4=Latino English, 5=Asian American English).",
           "dataType": "sc:Integer",
           "source": {
             "fileObject": {"@id": "speakers-csv"},
@@ -204,7 +204,7 @@
           "@type": "cr:Field",
           "@id": "speakers/age",
           "name": "age",
-          "description": "Speaker age bracket (1=younger, 2=older).",
+          "description": "Speaker age bracket (1=under 45, 2=55 or older).",
           "dataType": "sc:Integer",
           "source": {
             "fileObject": {"@id": "speakers-csv"},
@@ -413,6 +413,44 @@
     "Crowdsourcing",
     "Synthetic data generation"
   ],
+  "rai:dataCollectionRawData": [
+    "Per-speaker source clips: for each of 100 US celebrity speakers, one reference clip (data/audio/reference/<id>R.wav) plus five additional clips labeled A through E (e.g., F01A, F01B, ..., F01E). All source clips are excerpts of publicly available interview and podcast recordings, curated directly by the dataset authors under an IRB-approved research protocol and resampled to 16 kHz mono float32 WAV. The 100 reference (R) clips are released in data/audio/reference/. The A through E source clips for the speakers being cloned or morphed are not redistributed as standalone files in this release; their identifiers appear in stimulus IDs (e.g., '3_F01B' indicates a Type 3 clone of speaker F01 generated from clip F01B). Speaker metadata is in data/speakers.csv. No aggregated external dataset URI is claimed.",
+    "Voice clones (stimulus types 3 and 5; data/audio/comparison/3_*.wav and 5_*.wav): synthetically generated using the Cartesia text-to-speech system (https://www.cartesia.ai), 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., '3_F01B' is seeded from the same F01B source clip used as the comparison in '2_F01B'). Type 3 = same-speaker clone (cloned voice paired with the reference of the same speaker). Type 5 = different-speaker clone (cloned voice paired with the reference of a different speaker).",
+    "Voice morphs (stimulus type 6; data/audio/comparison/6_*.wav): synthetically generated using the voice-morphing feature of the same Cartesia system. 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, with 10 morph scales between 0 and 1 plus 1 anchor at scale 1, yielding 4 x 2 x 10 + 1 = 81 stimuli per reference speaker (8,100 total). Stimulus IDs encode the two endpoint speakers, the seed-recording variants, and the scale (e.g., '6_M05A_M03A_065' = morph between M05 and M03 with seed recordings A from each, at scale 65). Per-stimulus trajectory metadata (endpoint speakers, seed-recording variants, scale) is in data/stimuli_interpol.csv.",
+    "Pretrained model checkpoints used to compute the embeddings in data/embeddings/ are listed in docs/model_table.md and retain their original licenses; see https://huggingface.co/datasets/sendfuze/vipbench/blob/main/docs/model_table.md."
+  ],
+  "rai:provenanceActivities": [
+    {
+      "@type": "rai:ProvenanceActivity",
+      "name": "Collection",
+      "description": "Per-speaker source clips for 100 US celebrity speakers collected from publicly available interview and podcast recordings: one reference clip (R) plus five additional source clips (A through E) per speaker. Speakers are stratified across 5 sociophonetic groups x 2 genders x 2 age brackets (5 speakers per cell). Curated by the dataset authors under an IRB-approved research protocol."
+    },
+    {
+      "@type": "rai:ProvenanceActivity",
+      "name": "Synthetic generation",
+      "description": "Voice clones (stimulus types 3 and 5) generated by Cartesia TTS, seeded from a natural source clip of the speaker being cloned. The seed clip for a Type 3 clone is the same source clip used as the comparison in the matched Type 2 pair; the seed clip for a Type 5 clone is the same source clip used as the comparison in the matched Type 4 pair. Voice morphs (stimulus type 6) generated using the voice-morphing feature of the same Cartesia system: for each of 100 reference speakers, the latent voice representation 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, yielding 4 x 2 x 10 + 1 = 81 stimuli per reference speaker (8,100 total)."
+    },
+    {
+      "@type": "rai:ProvenanceActivity",
+      "name": "Preprocessing",
+      "description": "All audio resampled to 16 kHz mono float32 WAV. Clips trimmed to approximately 6 seconds. Speaker IDs assigned in the form F01-F50, M01-M50."
+    },
+    {
+      "@type": "rai:ProvenanceActivity",
+      "name": "Annotation",
+      "description": "1,290 English-speaking adult crowdworkers recruited via the Centaur AI platform answered binary same/different identity judgments on 9,800 voice pairs (median 10 judgments per pair, range 8 to 92). Each trial presented reference + 1-second silence + beep + comparison; listeners answered (a) binary same/different and (b) optional speaker recognition. Conducted under IRB approval with informed consent."
+    },
+    {
+      "@type": "rai:ProvenanceActivity",
+      "name": "Aggregation",
+      "description": "Per-pair P(same) and same/different vote counts computed by aggregating individual responses (data/stimuli.csv). Per-listener attention-check qualification flags computed and shipped alongside raw responses (data/participant_responses.csv). Spearman-Brown corrected split-half reliability rho_SB = 0.705 computed over 100 random splits of the 1,290-participant pool."
+    },
+    {
+      "@type": "rai:ProvenanceActivity",
+      "name": "Embedding extraction",
+      "description": "Utterance-level embeddings extracted from each of 9,900 audio files using 10 publicly available pretrained models (x-vector, ECAPA-TDNN, RawNet3, TitaNet, resemblyzer, wav2vec 2.0, HuBERT, WavLM, XLS-R, Whisper). For self-supervised models, both the final-layer embedding and per-transformer-layer mean-pooled embeddings are released. Extraction scripts are in code/extract_*.py."
+    }
+  ],
   "rai:dataCollectionMissingValues": "The know_speaker field is missing for some early-trial responses (less than 1% of records). Listeners with fewer than the qualification threshold of attention-check passes are flagged but their responses are still released.",
   "rai:dataCollectionTimeFrame": {
     "@type": "DateTime",