Publications

Abstract

The task of automatic playlist continuation is generating a list of recommended tracks that can be added to an existing playlist. By suggesting appropriate tracks, i. e., songs to add to a playlist, a recommender system can increase the user engagement by making playlist creation easier, as well as extending listening beyond the end of current playlist. The ACM Recommender Systems Challenge 2018 focuses on such task. Spotify released a dataset of playlists, which includes a large number of playlists and associated track listings. Given a set of playlists from which a number of tracks have been withheld, the goal is predicting the missing tracks in those playlists. We participated in the challenge as the team Unconscious Bias and, in this paper, we present our approach. We extend adversarial autoencoders to the problem of automatic playlist continuation. We show how multiple input modalities, such as the playlist titles as well as track titles, artists and albums, can be incorporated in the playlist continuation task.

Abstract

Within the first few years of life, children acquire many of the building blocks of their native language. This not only involves knowledge about the linguistic structure of spoken language, but also knowledge about the way in which this linguistic structure surfaces in their speech input. In this chapter, we review how infants and toddlers cope with differences between speakers and accents. Within the context of milestones in early speech perception, we examine how voice and accent characteristics are integrated during language processing, looking closely at the advantages and disadvantages of speaker and accent familiarity, surface-level deviation between two utterances, variability in the input, and prior speaker exposure. We conclude that although deviation from the child’s standard can complicate speech perception early in life, young listeners can overcome these additional challenges. This suggests that early spoken language processing is flexible and adaptive to the listening situation at hand.

Abstract

Languages combine arbitrary and iconic signals. How do iconic signals emerge and when do they persist? We present an experimental study of the role of iconicity in the emergence of structure in an artificial language. Using an iterated communication game in which we control the signalling medium as well as the meaning space, we study the evolution of communicative signals in transmission chains. This sheds light on how affordances of the communication medium shape and constrain the mappability and transmissibility of form-meaning pairs. We find that iconic signals can form the building blocks for wider compositional patterns

Abstract

Before infants become mature speakers of their native language, they must acquire a robust word-recognition system which allows them to strike the balance between allowing some variation (mood, voice, accent) and recognizing variability that potentially changes meaning (e.g. cat vs hat). The current meta-analysis quantifies how the latter, termed mispronunciation sensitivity, changes over infants’ first three years, testing competing predictions of mainstream language acquisition theories. Our results show that infants were sensitive to mispronunciations, but accepted them as labels for target objects. Interestingly, and in contrast to predictions of mainstream theories, mispronunciation sensitivity was not modulated by infant age, suggesting that a sufficiently flexible understanding of native language phonology is in place at a young age.

Abstract

Distributional vowel training for adults has been reported as “effective” for Spanish and Bulgarian learners of Dutch vowels, in studies using a behavioural task. A recent study did not yield a similar clear learning effect for Dutch learners of the English vowel contrast /æ/~/ε/, as measured with event-related potentials (ERPs). The present study aimed to examine the possibility that the latter result was related to the method. As in the ERP study, we tested whether distributional training improved Dutch adult learners’ perception of English /æ/~/ε/. However, we measured behaviour instead of ERPs, in a design identical to that used in the previous studies with Spanish learners. The results do not support an effect of distributional training and thus “replicate” the ERP study. We conclude that it remains unclear whether distributional vowel training is effective for Dutch adults.

Abstract

This study investigates the influence of both phonotactic and acoustic cues on the segmentation of spoken English. Listeners detected embedded English words in nonsense sequences (word spotting). Words aligned with phonotactic boundaries were easier to detect than words without such alignment. Acoustic cues to boundaries could also have signaled word boundaries, especially when word onsets lacked phonotactic alignment. However, only one of several durational boundary cues showed a marginally significant correlation with response times (RTs). The results suggest that word segmentation in English is influenced primarily by phonotactic constraints and only secondarily by acoustic aspects of the speech signal.

Abstract

Previous research has shown that listeners make use of their knowledge of phonotactic constraints to segment speech into individual words. The present study investigates the influence of phonotactics when segmenting a non-native language. German and English listeners detected embedded English words in nonsense sequences. German listeners also had knowledge of English, but English listeners had no knowledge of German. Word onsets were either aligned with a syllable boundary or not, according to the phonotactics of the two languages. Words aligned with either German or English phonotactic boundaries were easier for German listeners to detect than words without such alignment. Responses of English listeners were influenced primarily by English phonotactic alignment. The results suggest that both native and non-native phonotactic constraints influence lexical segmentation of a non-native, but familiar, language.

Abstract

In the word-learning domain, both adults and young children are able to find the correct referent of a word from highly ambiguous contexts that involve many words and objects by computing distributional statistics across the co-occurrences of words and referents at multiple naming moments (Yu & Smith, 2007; Smith & Yu, 2008). However, there is still debate regarding how learners accumulate distributional information to learn object labels in natural learning environments, and what underlying learning mechanism learners are most likely to adopt. Using the Human Simulation Paradigm (Gillette, Gleitman, Gleitman & Lederer, 1999), we found that participants’ learning performance gradually improved and that their ability to remember and carry over partial knowledge from past learning instances facilitated subsequent learning. These results support the statistical learning model that word learning is a continuous process.