Acoustic analysis supports the existence of a single distributional learning mechanism in structural rule learning from an artificial language

Acoustic analysis supports the existence of a single distributional learning mechanism in structural rule learning from an artificial language Okko Rasanen (okko.rasanen@aalto.fi) Department of Signal Processing and Acoustics, Aalto University, Otakaari 5 A, FI-00076 Aalto FINLAND Heikki Rasilo (heikki.rasilo@aalto.fi) Department of Signal Processing and Acoustics, Aalto University, Otakaari 5 A, FI-00076 Aalto FINLAND artificial nonsense language consisting of three-syllabic CVCVCV words with the middle syllable being always randomly selected from a pool of “fillers”, but the first and last syllable occurring always together (hence a “high- probability word”). It has been found out that when human listeners are familiarized with a continuous stream of such language without gaps between the high-probability words, and then later tested for preference between three-syllabic words that have different TPs between the syllables in terms of the familiarization stream, the listeners seem to prefer words that have occurred with higher internal TPs in the familiarization stream (Endress & Bonatti, 2007; Pena et al. 2002). However, introduction of 25 ms subliminal segments of silence between the high-probability words in the familiarization stream leads to a notable change in the learning outcome: the listeners start to prefer word forms that do not necessarily have the highest TPs across all syllables in the word. Instead, the preferred words may contain partially novel surface form but have dependencies between syllables that can be explained by abstract rules that are also valid for the words in the familiarization stream (Endress & Bonatti, 2007; Pena et al. 2002). The above finding is somewhat unexpected from the perspective of distributional learning at a linguistic level. The learning results between continuous and gapped familiarization streams should not differ as long as the perceived linguistic units and their ordering in the two conditions do not differ either. The result is also counterintuitive due to the fact that the gaps are tiny in duration in comparison to the other relevant signal segments such as syllables, and since CV-syllable based languages already contain natural silences associated with closures of plosives (e.g., word “#pura#ki”, where # denotes a closure). Pena et al. (2002) and Endress and Bonatti (2007) suggest that the additional silent gaps provide direct (but unconscious) cues to the segmentation of words from speech, freeing computational resources to structural learning of rule-like relations between constituents of the words. On the contrary, the absence of the gaps necessitates that the segmentation has to be first learned from the data (Endress & Bonatti, 2007; but see also discussion in Laakso & Calvo, 2011). It is therefore argued that the change in learning outcomes after introduction of the gaps provides evidence for non-distributional learning of structural relations between syllabic units (Bonatti & Endress, 2007). Abstract Research on artificial language acquisition has shown that insertion of short subliminal gaps to a continuous stream of speech has a notable effect on how human listeners interpret speech tokens constructed from syllabic constituents of the language. It has been argued that the observed results cannot be explained by a single statistical learning mechanism. On the other hand, computational simulations have shown that as long as the gaps are treated as structurally significant units of the language, a single distributional learning model can explain the behavioral results. However, the reason why the subliminal gaps interfere with processing of language at a linguistic level is currently unknown. In the current work, we concentrate on analyzing distributional properties of purely acoustic representations of speech, showing that a system performing unsupervised learning of transition probabilities between short-term acoustic events can replicate the main behavioral findings without a priori linguistic knowledge. Keywords: language acquisition; pattern discovery; distributional learning; acoustic analysis; lexical learning Introduction There is an ongoing debate regarding the degree that distributional learning mechanisms can explain aspects of language acquisition from speech, and the degree that rule- based mental processes are required in the task (e.g., Endress & Bonatti, 2007; Laakso & Calvo, 2011; Pena et al. 2002). Experimental studies with human test subjects have shown that both infants and adults are able to learn statistical regularities in continuously spoken artificial languages and use these regularities to segment speech into word-like units (e.g., Pena et al. 2002; Saffran, Aslin & Newport, 1996). Based on these findings, it has been suggested that the listeners may be using transitional probabilities (TPs) between speech units such as phones or syllables in order to discover statistically regular segments of speech (e.g., Saffran et al., 1996). Computational simulations have also verified that the TPs between signal events can be used to discover word-like units from continuous speech, and that these units do not necessarily need to be linguistic or phonetic in nature (Rasanen, 2011). Of especial interest is the degree that distributional learning can explain the learning of non-adjacent dependencies in a language. In earlier work, the learning of non-adjacent dependencies has been studied using an