N4 (NATO Native and Non Native) database
Notice bibliographique
Résumé
Speech technology is covering an increasing number of languages, and systems are becoming more robust with regard to speech variability such as speaking style and accents. However, for real applications, especially in a multilingual and multinational context, further robustness to regional and even non-native accents is necessary. Among numerous corpora available for speech research few have specifically addressed this issue. The NATO Speech and Language Technology group decided to create a corpus geared towards the study of non-native accents. The group chose naval communications as the common task because it naturally includes a great deal of non-native speech and because there were training facilities where data could be collected in several countries. The N4 NATO Native and Non-Native Speech corpus was developed by the NATO research group on Speech and Language Technology in order to provide a military-oriented database for multilingual and non-native speech processing studies. Speech data was recorded in the naval transmission training centers of four countries (Germany, The Netherlands, United Kingdom, and Canada) during naval communication training sessions in 2000-2002. The material consists of native and non-native speakers using NATO Naval English procedure between ships where the typical sentence sounds like “This is alpha, whiskey, roger. I make two seven zero six hostile, two seven zero six. Out”, and reading from a text, "The North Wind and the Sun," in both English and the speaker's native language. The audio material was recorded on DAT and downsampled to 16kHz-16bit, and all the audio files have been manually transcribed and annotated with speakers identities using the Transcriber tool. Navy procedure recordings and text readings have been stored in different files. The first digit in the filename indicates the type of speech. Among speech segments, the duration of Navy procedure recordings range from 1.3 to 2.3 hours for a total of 7.5 hours. The duration of the native language text readings range from 1.5 minutes to 22.9 minutes for a total of around one hour. Canada Germany The Netherlands United Kingdom All Signal 5.30 3.20 5.00 6.30 19.80 Silence 3.00 0.56 2.00 4.70 Speech 2.30 2.64 3.00 1.60 Speech 2.30 2.64 3.00 1.60 9.54 Navy proc 2.00 1.90 2.30 1.30 Read text 0.30 0.74 0.70 0.30 Read text 0.30 0.74 0.70 0.30 2.04 Non-native 0.27 0.37 0.32 0.00 Native 0.03 0.37 0.38 0.30 The database contains the following information about each speaker: gender, age, weight, length, possible speaking or hearing disorders, education level, living area, accent, second language, the year English was learned(for non-native speakers). The speaker accents vary widely from country to country. The speaker's average age was 22.6 years. Nineteen women participated, accounting for 18% of the study participants. There were a total of 115 speakers. Canada Germany The Netherlands United Kingdom All #Speakers 22 51 31 11 115 #Women 5 0 9 5 19 Age 22-35 17-23 17-61 19-62 17-62 Age mean 28.3 20.1 21 27.5 22.6
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,002 | 0,001 |
| Bibliométrie | 0,005 | 0,006 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,002 | 0,002 |
| Science ouverte | 0,003 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,031 | 0,048 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».