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Record W7002482260

N4 (NATO Native and Non Native) database

2007· other· en· W7002482260 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueAmericanae (AECID Library) · 2007
Typeother
Languageen
FieldArts and Humanities
TopicArchaeological and Geological Studies
Canadian institutionsnot available
Fundersnot available
KeywordsNasalizationMicrophoneFilter (signal processing)NucleofectionWork (physics)
DOInot available

Abstract

fetched live from OpenAlex

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. <table border="0" width="100%" cellspacing="0" cellpadding="2" class="infoBoxContents"><tr align=center><td> </td><td>Canada</td><td>Germany</td><td>The Netherlands</td><td>United Kingdom</td><td>All</td></tr><tr align=center><td align=left><strong>Signal</strong></td><td>5.30</td><td>3.20</td><td>5.00</td><td>6.30</td><td>19.80</td></tr><tr align=center><td align=right>Silence</td><td>3.00</td><td>0.56</td><td>2.00</td><td>4.70</td></tr><tr align=center><td align=right>Speech</td><td>2.30</td><td>2.64</td><td>3.00</td><td>1.60</td></tr><tr align=center><td align=left><strong>Speech</strong></td><td>2.30</td><td>2.64</td><td>3.00</td><td>1.60</td><td>9.54</td></tr><tr align=center><td align=right>Navy proc</td><td>2.00</td><td>1.90</td><td>2.30</td><td>1.30</td></tr><tr align=center><td align=right>Read text</td><td>0.30</td><td>0.74</td><td>0.70</td><td>0.30</td></tr><tr align=center><td align=left><strong>Read text</strong></td><td>0.30</td><td>0.74</td><td>0.70</td><td>0.30</td><td>2.04</td></tr><tr align=center><td align=right>Non-native</td><td>0.27</td><td>0.37</td><td>0.32</td><td>0.00</td></tr><tr align=center><td align=right>Native</td><td>0.03</td><td>0.37</td><td>0.38</td><td>0.30</td></tr></table>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. <table border="0" width="100%" cellspacing="0" cellpadding="2" class="infoBoxContents"><tr align=center><td></td><td>Canada</td><td>Germany</td><td>The Netherlands</td><td>United Kingdom</td><td>All</td></tr><tr align=center><td align=left><strong>#Speakers</strong></td><td>22</td><td>51</td><td>31</td><td>11</td><td>115</td></tr><tr align=center><td align=left><strong>#Women</strong></td><td>5</td><td>0</td><td>9</td><td>5</td><td>19</td></tr><tr align=center><td align=left><strong>Age</strong></td><td>22-35</td><td>17-23</td><td>17-61</td><td>19-62</td><td>17-62</td></tr><tr align=center><td align=left><strong>Age mean</strong></td><td>28.3</td><td>20.1</td><td>21</td><td>27.5</td><td>22.6</td></tr></table>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.123
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.003
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0630.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.227
Teacher spread0.206 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it