| 1 |
Models and activations for "Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? - A computational investigation" ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 2 |
Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? -- A computational investigation ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 3 |
Models and activations for "Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? - A computational investigation" ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 4 |
Can phones, syllables, and words emerge as side-products of cross-situational audiovisual learning? - A computational investigation ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 5 |
A thorough evaluation of the Language Environment Analysis (LENA) system
|
|
|
|
In: Behav Res Methods (2021)
|
|
BASE
|
|
Show details
|
|
|
| 7 |
A thorough evaluation of the Language Environment Analysis (LENA) system
|
|
|
|
In: ISSN: 1554-351X ; EISSN: 1554-3528 ; Behavior Research Methods ; https://hal.archives-ouvertes.fr/hal-03095997 ; Behavior Research Methods, Psychonomic Society, Inc, 2020, 53 (2), pp.467-486. ⟨10.3758/s13428-020-01393-5⟩ (2020)
|
|
Abstract:
International audience ; In the previous decade, dozens of studies involving thousands of children across several research disciplines have made use of a combined daylong audio-recorder and automated algorithmic analysis called the LENA system, which aims to assess children's language environment. While the system's prevalence in the language acquisition domain is steadily growing, there are only scattered validation efforts on only some of its key characteristics. Here, we assess the LENA system's accuracy across all of its key measures: speaker classification, Child Vocalization Counts (CVC), Conversational Turn Counts (CTC), and Adult Word Counts (AWC). Our assessment is based on manual annotation of clips that have been randomly or periodically sampled out of daylong recordings, collected from (a) populations similar to the system's original training data (North American English-learning children aged 3-36 months), (b) children learning another dialect of English (UK), and (c) slightly older children growing up in a different linguistic and socio-cultural setting (Tsimane' learners in rural Bolivia). We find reasonably high accuracy in some measures (AWC, CVC), with more problematic levels of performance in others (CTC, precision of male adults and other children). Statistical analyses do not support the view that performance is worse for children who are dissimilar from the LENA original training set. Whether LENA results are accurate enough for a given research, educational, or clinical application depends largely on the specifics at hand. We therefore conclude with a set of recommendations to help researchers make this determination for their goals.
|
|
Keyword:
[SCCO.LING]Cognitive science/Linguistics; [SCCO.PSYC]Cognitive science/Psychology
|
|
URL: https://hal.archives-ouvertes.fr/hal-03095997/file/main_document.pdf
https://hal.archives-ouvertes.fr/hal-03095997
https://hal.archives-ouvertes.fr/hal-03095997/document
https://doi.org/10.3758/s13428-020-01393-5
|
|
BASE
|
|
Hide details
|
|
|
| 8 |
Analysis of Predictive Coding Models for Phonemic Representation Learning in Small Datasets ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 9 |
ALICE: An open-source tool for automatic measurement of phoneme, syllable, and word counts from child-centered daylong recordings
|
|
|
|
In: Behav Res Methods (2020)
|
|
BASE
|
|
Show details
|
|
|
| 10 |
A computational model of early language acquisition from audiovisual experiences of young infants ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 11 |
SylNet: An Adaptable End-to-End Syllable Count Estimator for Speech ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 12 |
Is infant-directed speech interesting because it is surprising? – Linking properties of IDS to statistical learning and attention at the prosodic level ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 13 |
Pre-linguistic segmentation of speech into syllable-like units ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 16 |
Making predictable unpredictable with style – Behavioral and electrophysiological evidence for the critical role of prosodic expectations in the perception of prominence in speech ...
|
|
|
|
BASE
|
|
Show details
|
|
|
| 17 |
Analyzing Distributional Learning of Phonemic Categories in Unsupervised Deep Neural Networks
|
|
|
|
BASE
|
|
Show details
|
|
|
|
|
