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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.

Modelling of early language acquisition aims to understand how infants bootstrap their language skills. The modelling encompasses properties of the input data used for training the models, the cognitive hypotheses and their algorithmic implementations being tested, and the evaluation methodologies to compare models to human data. Recent developments have enabled the use of more naturalistic training data for computational models. This also motivates development of more naturalistic tests of model behaviour. A crucial step towards such an aim is to develop representative speech datasets consisting of speech heard by infants in their natural environments. However, a major drawback of such recordings is that they are typically noisy, and it is currently unclear how the sound quality could affect analyses and modelling experiments conducted on such data. In this paper, we explore this aspect for the case of infant-directed speech (IDS) and adult-directed speech (ADS) analysis. First, we manually and automatically annotated audio quality of utterances extracted from two corpora of child-centred long-form recordings (in English and French). We then compared acoustic features of IDS and ADS in an in-lab dataset and across different audio quality subsets of naturalistic data. Finally, we assessed how the audio quality and recording environment may change the conclusions of a modelling analysis using a recent self-supervised learning model. Our results show that the use of modest and high audio quality naturalistic speech data result in largely similar conclusions on IDS and ADS in terms of acoustic analyses and modelling experiments. We also found that an automatic sound quality assessment tool can be used to screen out useful parts of long-form recordings for a closer analysis with comparable results to that of manual quality annotation.

When assessing children in laboratory experiments, the measured responses also contain task-irrelevant participant-level variability (“noise”) and measurement noise. Since experimental data are used to make inferences of development of cognitive capabilities with age, it is important to know if reliability of the used measurements depends on child age. Any systematic age-dependent changes in reliability could result in misleading developmental trajectories, as lower reliability will necessarily result in smaller effect sizes. This paper examines age-dependency of task-independent measurement variability in early childhood (3–40 months) by analyzing two large-scale datasets of participant-level experimental responses: the ManyBabies infant-directed speech preference (MB-IDS) dataset and a saccadic reaction time (SRT) dataset collected from rural South Africa. Analysis of participant- and study-level data reveals that MB-IDS shows comparable reliability across the included age range. In contrast, SRTs reflect systematically increasing measurement consistency with increasing age. Potential reasons and implications of this divergence are briefly discussed.

Previous computational models of early language acquisition have shown how linguistic structure of speech can be acquired using auditory or audiovisual learning mechanisms. However, real infants have sustained access to both uni- and multimodal sensory experiences. Therefore, it is of interest how the uni- and multimodal learning mechanisms could operate in concert, and how their interplay might affect the acquisition dynamics of different linguistic representations. This paper explores these questions with a computational model capable of simultaneous auditory and audiovisual learning from speech and images. We study how the model’s latent representations reflect phonemic, lexical, and semantic knowledge as a function of language experience. We also test how the findings vary with differential emphasis on the two learning mechanisms. As a result, we find phonemic learning always starting to emerge before lexical learning, followed by semantics. However, there is also notable overlap in their development. The same pattern emerges irrespectively of the emphasis on auditory or audiovisual learning. The result illustrates how the acquisition dynamics of linguistic representations are decoupled from the primary learning objectives (mechanisms) of the learner, and how the emergence of phonemes and words can be facilitated by both auditory and audiovisual learning in a synergetic manner.

The exaggerated intonation and special rhythmic properties ofinfant-directed speech (IDS) have been hypothesized to attractinfant’s attention to the speech stream. However, studiesinvestigating IDS in the context of models of attention arefew. A number of such models suggest that surprising ornovel perceptual inputs attract attention, where novelty can beoperationalized as the statistical predictability of the stimulusin a context. Since prosodic patterns such as F0 contours areaccessible to young infants who are also adept statisticallearners, the present paper investigates a hypothesis that pitchcontours in IDS are less predictable than those in adult-directed speech (ADS), thereby efficiently tapping into thebasic attentional mechanisms of the listeners. Results fromanalyses with naturalistic IDS and ADS speech show that IDShas lower overall predictability of intonation acrossneighboring syllables even when the F0 contours in bothspeaking styles are normalized to the same frequency range.

Infants’ speech perception adapts to the phonemic categoriesof their native language, a process assumed to be driven bythe distributional properties of speech. This study investigateswhether deep neural networks (DNNs), the current state-of-the-art in distributional feature learning, are capable oflearning phoneme-like representations of speech in anunsupervised manner. We trained DNNs with unlabeled andlabeled speech and analyzed the activations of each layer withrespect to the phones in the input segments. The analysesreveal that the emergence of phonemic invariance in DNNs isdependent on the availability of phonemic labeling of theinput during the training. No increased phonemic selectivityof the hidden layers was observed in the purely unsupervisednetworks despite successful learning of low-dimensionalrepresentations for speech. This suggests that additionallearning constraints or more sophisticated models are neededto account for the emergence of phone-like categories indistributional learning operating on natural speech.

Recent work has proposed that prominence perception inspeech could be driven by predictability of prosodic patterns,connecting prominence perception to the concept of statisticallearning. In the present study, we tested the predictabilityhypothesis by conducting a listening test where subjects werefirst exposed to a 5-minute stream of sentences with a certainproportion of sentence-final words having either a falling orrising pitch trajectory. After the exposure stage, subjects wereasked to grade prominence in a set of novel sentences withsimilar pitch patterns. The results show that the subjects weresignificantly more likely to perceive words with low-probability pitch trajectories as prominent independently ofthe direction of the pitch change. This suggests that evenshort exposure to prosodic patterns with a certain statisticalstructure can induce changes in prominence perception,supporting the connection between prominence perceptionand attentional orientation towards low-probability events inan otherwise predictable context.