Age-Dependent Analysis and Stochastic Generation of Child-Directed Speech
- URL: http://arxiv.org/abs/2405.07700v1
- Date: Mon, 13 May 2024 12:35:10 GMT
- Title: Age-Dependent Analysis and Stochastic Generation of Child-Directed Speech
- Authors: Okko Räsänen, Daniil Kocharov,
- Abstract summary: We present an approach to model age-dependent linguistic properties of child-directed speech (CDS) using a language model (LM) trained on CDS transcripts and ages of the recipient children.
We compare characteristics of the generated CDS against the real speech addressed at children of different ages, showing that the LM manages to capture age-dependent changes in CDS.
- Score: 10.369750912567714
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Child-directed speech (CDS) is a particular type of speech that adults use when addressing young children. Its properties also change as a function of extralinguistic factors, such as age of the child being addressed. Access to large amounts of representative and varied CDS would be useful for child language research, as this would enable controlled computational modeling experiments of infant language acquisition with realistic input in terms of quality and quantity. In this study, we describe an approach to model age-dependent linguistic properties of CDS using a language model (LM) trained on CDS transcripts and ages of the recipient children, as obtained from North American English corpora of the CHILDES database. The created LM can then be used to stochastically generate synthetic CDS transcripts in an age-appropriate manner, thereby scaling beyond the original datasets in size. We compare characteristics of the generated CDS against the real speech addressed at children of different ages, showing that the LM manages to capture age-dependent changes in CDS, except for a slight difference in the effective vocabulary size. As a side product, we also provide a systematic characterization of age-dependent linguistic properties of CDS in CHILDES, illustrating how all measured aspects of the CDS change with children's age.
Related papers
- Personalized Speech Recognition for Children with Test-Time Adaptation [21.882608966462932]
Off-the-shelf automatic speech recognition (ASR) models primarily pre-trained on adult data tend to generalize poorly to children's speech.
We devised a novel ASR pipeline to apply unsupervised test-time adaptation (TTA) methods for child speech recognition.
Our results show that ASR models adapted with TTA methods significantly outperform the unadapted off-the-shelf ASR baselines both on average and statistically across individual child speakers.
arXiv Detail & Related papers (2024-09-19T21:40:07Z) - Is Child-Directed Speech Effective Training Data for Language Models? [34.46268640655943]
We train GPT-2 and RoBERTa models on 29M words of English child-directed speech.
We test whether the global developmental ordering or the local discourse ordering of children's training data supports high performance relative to other datasets.
These findings support the hypothesis that, rather than proceeding from better data, the child's learning algorithm is substantially more data-efficient than current language modeling techniques.
arXiv Detail & Related papers (2024-08-07T08:18:51Z) - An Initial Investigation of Language Adaptation for TTS Systems under Low-resource Scenarios [76.11409260727459]
This paper explores the language adaptation capability of ZMM-TTS, a recent SSL-based multilingual TTS system.
We demonstrate that the similarity in phonetics between the pre-training and target languages, as well as the language category, affects the target language's adaptation performance.
arXiv Detail & Related papers (2024-06-13T08:16:52Z) - Syntactic Language Change in English and German: Metrics, Parsers, and Convergences [56.47832275431858]
The current paper looks at diachronic trends in syntactic language change in both English and German, using corpora of parliamentary debates from the last c. 160 years.
We base our observations on five dependencys, including the widely used Stanford Core as well as 4 newer alternatives.
We show that changes in syntactic measures seem to be more frequent at the tails of sentence length distributions.
arXiv Detail & Related papers (2024-02-18T11:46:16Z) - Understanding Spoken Language Development of Children with ASD Using
Pre-trained Speech Embeddings [26.703275678213135]
Natural Language Sample (NLS) analysis has gained attention as a promising complement to traditional methods.
This paper proposes applications of speech processing technologies in support of automated assessment of children's spoken language development.
arXiv Detail & Related papers (2023-05-23T14:39:49Z) - Analysing the Impact of Audio Quality on the Use of Naturalistic
Long-Form Recordings for Infant-Directed Speech Research [62.997667081978825]
Modelling of early language acquisition aims to understand how infants bootstrap their language skills.
Recent developments have enabled the use of more naturalistic training data for computational models.
It is currently unclear how the sound quality could affect analyses and modelling experiments conducted on such data.
arXiv Detail & Related papers (2023-05-03T08:25:37Z) - Understanding Translationese in Cross-Lingual Summarization [106.69566000567598]
Cross-lingual summarization (MS) aims at generating a concise summary in a different target language.
To collect large-scale CLS data, existing datasets typically involve translation in their creation.
In this paper, we first confirm that different approaches of constructing CLS datasets will lead to different degrees of translationese.
arXiv Detail & Related papers (2022-12-14T13:41:49Z) - Towards Language Modelling in the Speech Domain Using Sub-word
Linguistic Units [56.52704348773307]
We propose a novel LSTM-based generative speech LM based on linguistic units including syllables and phonemes.
With a limited dataset, orders of magnitude smaller than that required by contemporary generative models, our model closely approximates babbling speech.
We show the effect of training with auxiliary text LMs, multitask learning objectives, and auxiliary articulatory features.
arXiv Detail & Related papers (2021-10-31T22:48:30Z) - Mandarin-English Code-switching Speech Recognition with Self-supervised
Speech Representation Models [55.82292352607321]
Code-switching (CS) is common in daily conversations where more than one language is used within a sentence.
This paper uses the recently successful self-supervised learning (SSL) methods to leverage many unlabeled speech data without CS.
arXiv Detail & Related papers (2021-10-07T14:43:35Z) - Word Acquisition in Neural Language Models [0.38073142980733]
We investigate how neural language models acquire individual words during training, extracting learning curves and ages of acquisition for over 600 words.
We find that the effects of concreteness, word length, and lexical class are pointedly different in children and language models.
arXiv Detail & Related papers (2021-10-05T23:26:16Z) - Learning to Understand Child-directed and Adult-directed Speech [18.29692441616062]
Human language acquisition research indicates that child-directed speech helps language learners.
We compare the task performance of models trained on adult-directed speech (ADS) and child-directed speech (CDS)
We find indications that CDS helps in the initial stages of learning, but eventually, models trained on ADS reach comparable task performance, and generalize better.
arXiv Detail & Related papers (2020-05-06T10:47:02Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.