Reliability of the Language Environment Analysis (LENA) in French-English Bilingual Speech
Bibliographic record
Abstract
Purpose: This study examined the utility of the Language ENvironment Analysis (LENA) recording system for investigating the language input to bilingual infants. Method: Twenty-one French-English bilingual families with a 10-month-old infant participated in this study. Using the LENA recording system, each family contributed three full days of recordings within a one-month period. A portion of these recordings (945 minutes) were manually transcribed, and the word counts from these transcriptions were compared against the LENA-generated adult word counts. Results: Data analyses reveal that the LENA algorithms were reliable in counting words in both Canadian English and Canadian French, even when both languages are present in the same recording. While the LENA system tended to underestimate the amount of speech in the recordings, there was a strong correlation between the LENA-generated and human transcribed adult word counts for each language. Importantly, this relationship holds when accounting for different-gendered and different-accented speech. Conclusions: The LENA recording system is a reliable tool for estimating word counts, even for bilingual input. Special considerations and limitations for using the LENA recording system in a bilingual population are discussed. These results open up possibilities for investigating caregiver talk to bilingual infants in more detail.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".