| Challenge: | linguistic properties of child-directed speech differ from adult-directed in many ways . linguistic differences between CDS and ADS are retained, but the acoustic properties are similar. |
| Approach: | They compare the task performance of models trained on adult-directed speech and child-directed language . they propose that CDS is optimized for learnability, but not for comprehension . |
| Outcome: | The proposed model trains on adult-directed speech and child-directed language . the model generalizes better on the training register and on synthesized speech . |
Similar Papers
Is Child-Directed Speech Effective Training Data for Language Models? (2024.emnlp-main)
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| Challenge: | High-performing language models are typically trained on hundreds of billions of words, but human learners use language fluently after far less training data. |
| Approach: | They train GPT-2 and RoBERTa models on 29M words of English child-directed speech and a new matched, synthetic dataset. |
| Outcome: | The proposed models show that child language input is not valuable for training language models. |
Evaluating and Improving Child-Directed Automatic Speech Recognition (2020.lrec-1)
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| Challenge: | a recent study shows that adult speech recognition systems are lagging behind child models due to the fact that children's vocal tracts are smaller than adults . |
| Approach: | They evaluate a model that trains on adult data and apply additional tuning to varied amounts of child speech data to improve child-directed speech recognition. |
| Outcome: | The proposed model improves over baseline models using child data and small amounts of child audio data. |
Multilingual Transfer Learning for Children Automatic Speech Recognition (2022.lrec-1)
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| Challenge: | Recent advances in automatic speech recognition (ASR) systems have been criticized for high acoustic variability and limited amount of available training data. |
| Approach: | They propose a two-step training strategy that uses multilingual learning followed by language-specific transfer learning to generalize children's speech. |
| Outcome: | The proposed training strategy outperforms single language training and multilingual and transfer learning alone in English. |
Child-Directed Language Does Not Consistently Boost Syntax Learning in Language Models (2025.emnlp-main)
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| Challenge: | prevailing view in language acquisition research has long held that child-directed language is more effective than adultdirected language (ADL) |
| Approach: | They propose a frequency-controlled testing methodology to enable balanced comparisons across training corpora. |
| Outcome: | The proposed method outperforms models trained on English Child-Directed Language (CDL) but it does not yield stronger generalizations for acquiring syntax. |
Learning from Child-directed Speech in Two-language Scenarios: A French-English Case-Study (2026.findings-eacl)
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| Challenge: | a systematic study of compact language models with limited computational resources is challenging for many research contexts and real-world applications. |
| Approach: | They extend BabyBERTa to English-French scenarios under strictly sizematched data conditions. |
| Outcome: | The proposed model extends to English-French scenarios under sizematched data conditions . the results show context-dependent effects of multilingual training . |
Word Acquisition in Neural Language Models (2022.tacl-1)
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| Challenge: | Language models acquire individual words during training, based on unigram token frequencies, before transitioning loosely to bigram probabilities, eventually converging on more nuanced predictions. |
| Approach: | They examine how neural language models acquire individual words during training, extracting learning curves and ages of acquisition for over 600 words on the MacArthur-Bates Communicative Development Inventory. |
| Outcome: | The models follow consistent patterns during training for both unidirectional and bidirectional models, and for both LSTM and Transformer architectures. |
Language acquisition: do children and language models follow similar learning stages? (2023.findings-acl)
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| Challenge: | During language acquisition, children follow a typical sequence of learning stages, whereby they first learn to categorize phonemes before they develop their lexicon and eventually master complex syntactic structures. |
| Approach: | They train 48 GPT-2 models from scratch and evaluate their syntactic and semantic abilities at each training step using 96 probes curated from the BLiMP, Zorro and BIG-Bench benchmarks. |
| Outcome: | The proposed model exhibits similar learning trajectories to human children aged between 18 months and 6 years. |
ChildMandarin: A Comprehensive Mandarin Speech Dataset for Young Children Aged 3-5 (2025.acl-long)
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Jiaming Zhou, Shiyao Wang, Shiwan Zhao, Jiabei He, Haoqin Sun, Hui Wang, Cheng Liu, Aobo Kong, Yujie Guo, Xi Yang, Yequan Wang, Yonghua Lin, Yong Qin
| Challenge: | Automatic speech recognition systems have advanced significantly with models like Whisper, Conformer, and self-supervised frameworks such as Wav2vec 2.0. |
| Approach: | They propose to use Mandarin speech datasets to analyze pronunciation and tone of children aged 3 to 5 and evaluate their models on speaker verification (SV) They find that the datasets are more robust than those used by adult speech recognition systems and are open-source and available for all academic purposes. |
| Outcome: | The proposed dataset includes 41.25 hours of speech with carefully crafted manual transcriptions, collected from 397 speakers across various provinces in China, with balanced gender representation. |
Representing the Toddler Lexicon: Do the Corpus and Semantics Matter? (2022.lrec-1)
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| Challenge: | Existing studies on child language development have relied on adult-based measures to model their lexicons. |
| Approach: | They propose to use transcripts of child-directed conversations, picture books and dialog from G-rated movies to approximate the language input a North American preschooler might hear. |
| Outcome: | The proposed model outperforms models based on the existing corpus and the existing model. |
How poor is the stimulus? Evaluating hierarchical generalization in neural networks trained on child-directed speech (2023.acl-long)
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| Challenge: | LSTMs and Transformers perform well at capturing the surface statistics of child-directed speech, but both model types generalize in a way consistent with an incorrect linear rule than the correct hierarchical rule. |
| Approach: | They train LSTMs and Transformers on text from the CHILDES corpus and evaluate what they learn about English yes/no questions. |
| Outcome: | The proposed models perform well at capturing the surface statistics of child-directed speech, but generalize more consistent with an incorrect linear rule than the correct hierarchical rule. |