Children's speech acquisition is influenced by universal and language-specific forces. Some speech error patterns (or phonological processes) in children's speech are observed in many languages, but the same error pattern may have different effects in different languages. We aimed to explore phonological effects of the same speech error patterns across different languages, target audiences and discourse modes, using a novel method for large-scale corpus investigation. As an additional aim, we investigated the face validity of five different phonological effect measures by relating them to subjective ratings of assumed effects on intelligibility, as provided by practicing speech-language pathologists. Six frequently attested speech error patterns were simulated in authentic corpus data: backing, fronting, stopping, /r/-weakening, cluster reduction and weak syllable deletion-each simulation resulting in a misarticulated version of the original corpus. Phonological effects were quantified using five separate metrics of phonological complexity and distance from expected target forms. Using Swedish child-speech data as a reference, phonological effects were compared between this reference and a) child speech in Norwegian and English, and b) data representing different modes of discourse (spoken/written) and target audiences (adults/children) in Swedish. Of the speech error patterns, backing-the one atypical pattern of those included-was found to cause the most detrimental effects, across languages as well as across modes and speaker ages. However, none of the measures reflects intuitive rankings as provided by clinicians regarding effects on intelligibility, thus corroborating earlier reports that phonological competence is not translatable into levels of intelligibility.

This study aimed to investigate concerns of validity and reliability in subjective ratings of age-of-acquisition (AoA), through exploring characteristics of the individual rater. An additional aim was to validate the obtained AoA ratings against two corpora – one of child speech and one of adult speech – specifically exploring whether words over-represented in the child-speech corpus are rated with lower AoA than words characteristic of the adult-speech corpus. The results show that less than one-third of participating informants’ ratings are valid and reliable. However, individuals with high familiarity with preschool-aged children provide more valid and reliable ratings, compared to individuals who do not work with or have children of their own. The results further show a significant, age-adjacent difference in rated AoA for words from the two different corpora, thus strengthening their validity. The study provides AoA data, of high specificity, for 100 child-specific and 100 adult-specific Swedish words.

We present an open-access analytic tool, which allows researchers to simultaneously control for and combine language data from the child, the caregiver, multiple languages, and across multiple time points to make inferences about the social and cognitive factors driving the shape of language development. We demonstrate how the tool works in three domains of language learning and across six languages. The results demonstrate the usefulness of this approach as well as providing deeper insight into three areas of language production and acquisition: egocentric language use, the learnability of nouns versus verbs, and imageability. We have made the Frequency Filter tool freely available as an R-package for other researchers to use at https://github.com/rosemm/FrequencyFilter.

This paper describes an approach to identifying speakers and addressees in dialogues extracted from literary fiction, along with a dataset annotated for speaker and addressee. The overall purpose of this is to provide annotation of dialogue interaction between characters in literary corpora in order to allow for enriched search facilities and construction of social networks from the corpora. To predict speakers and addressees in a dialogue, we use a sequence labeling approach applied to a given set of characters. We use features relating to the current dialogue, the preceding narrative, and the complete preceding context. The results indicate that even with a small amount of training data, it is possible to build a fairly accurate classifier for speaker and addressee identification across different authors, though the identification of addressees is the more difficult task.

Child-directed spoken data is the ideal source of support for claims about children’s linguistic environments. However, phonological transcriptions of child-directed speech are scarce,compared to sources like adult-directed speech or text data. Acquiring reliable descriptions of children’s phonological environments from more readily accessible sources would mean considerable savings of time and money. The first step towards this goal is to quantify the reliability of descriptions derived from such secondary sources. We investigate how phonological distributions vary across different modalities (spoken vs. written), and across the age of the intended audience (children vs. adults). Using a previously unseen collection of Swedish adult- and child-directed spoken and written data, we combine lexicon look-up and grapheme-to-phonemeconversion to approximate phonological characteristics. The analysis shows distributional differences across datasets both for single phonemes and for longer phoneme sequences. Some of these are predictably attributed to lexical and contextual characteristics of text vs. speech.The generated phonological transcriptions are remarkably reliable. The differences in phonological distributions between child-directed speech and secondary sources highlight a need for compensatory measures when relying on written data or onadult-directed spoken data, and/or for continued collection ofactual child-directed speech in research on children’s language environments.

Non-verbal cues from speakers, such as eye gaze and hand positions, play an important role in word learning. This is consistent with the notion that for meaning to be reconstructed, acoustic patterns need to be linked to time-synchronous patterns from at least one other modality. In previous studies of a multimodally annotated corpus of parent–child interaction, we have shown that parents interacting with infants at the early word-learning stage (7–9 months) display a large amount of time-synchronous patterns, but that this behaviour tails off with increasing age of the children. Furthermore, we have attempted to quantify the informativeness of the different nonverbal cues, that is, to what extent they actually help to discriminate between different possible referents, and how critical the timing of the cues is. The purpose of this paper is to generalise our earlier model by quantifying informativeness resulting from non-verbal cues occurring both before and after their associated verbal references.

For the second year in a row we brought two related themes of NLP for Computer-Assisted Language Learning and NLP for Language Acquisition together. The goal of organizing joint workshops is to provide a meeting place for researchers working on language learning issues including both empirical and experimental studies and NLP-based applications. The resulting volume covers a variety of topics from the two fields and - hopefully - showcases the challenges and achievements in the field.

The seven papers in this volume cover native language identification in learner writings, using syntactic complexity development in language learner language to identify reading comprehension texts of appropriate level, exploring the potential of parallel corpora to predict mother-language specific problem areas for learners of another language, tools for learning languages - both well-resourced ones such as English as well as endangered or under-resourced ones such as Yakut and Võro, as well as exploring the potential of automatically identifying and correcting word-level errors in Swedish learner writing.