This study reports on the first investigation into the learnability of tone spread and shift patterns, as determined by computer simulations of the learning process. Our simulations are cast within the context of a synchronic analytical framework developed in earlier work. The framework uses licensing constraints and foot structure to drive various kinds of tonal reassociation. One problem with the framework was that it was able to generate various unattested patterns. We address this problem through the learnability simulations; our results show that the representable-but-unattested patterns are harder to learn, explaining their non-attestation. This way, we demonstrate that learnability simulations are a meaningful tool for a typological account of tonal phenomena.
This paper proposes an approach to bounded tone shift and spread as found in Bantu languages. Its core intuition is that the bounding domain is delimited by foot structure. The approach uses layered foot representations to capture ternary phenomena, following Martinez-Paricio & Kager (2015). A set of licensing and structural constraints regulate tone-foot interactions. Harmonic Serialism is adopted as the grammatical framework, to allow for an account of opaque patterns (Prince & Smolensky 1993/2004McCarthy 2010a).The present approach improves on previous accounts in two ways. Firstly, the size of the tonal bounding domain follows from independently motivated foot representations, rather than being stipulated in the constraint set. Secondly, the approach obviates the need for markedness constraints that refer to underlying structure, because all relevant lexical information is reflected in foot structures.The approach is demonstrated on Saghala (Patin 2009). Saghala shows both shift and spread in a trisyllabic domain. There are six tone patterns, dependent on the contact or near-contact of tones, and the position of word boundaries. An analysis is presented that accounts for all patterns. The success of the analysis shows that the foot-based approach is equipped to deal with a variety of bounded tone phenomena.
We identify evidence supporting two amendments to standard metrical theory: the inclusion of layered feet, and the allowance of syllable-integrity violations, where a foot parses some, but not all, of a syllable’s constituents. The evidence comes from a High tone spreading process attested in Copperbelt Bemba (CB), which as reported by Bickmore and Kula (2013) et seq., occurs over a ternary domain. In quintessentially metrical fashion, the domain is sensitive to the presence and position of heavy syllables. Thus, we argue that metrical theory should take the CB data into account.CB ternary spreading can occur in contexts with an abundance of unparsed syllables on either side of the domain. We argue that this property is problematic for ‘Weak Layering’ accounts using binary feet (McCarthy and Prince 1986; Hayes 1995), which revolve around the minimal presence of unparsed syllables. We propose an alternative account using layered feet (Martínez-Paricio and Kager 2015), specifying an inner quantity-sensitive iamb and a strictly monomoraic adjunct. We show that a principled characterization of the spreading domain is that tone associates to all and only footed moras. We argue that a metrical analysis provides a more principled account of the data than can be achieved by Bickmore and Kula’s purely autosegmental analysis.Finally, we show that foot-based accounts of CB ternary spreading predict syllable-integrity violations (SIVs), where parsing consumes only the first of two tautosyllabic moras. Contrary to the common view that SIVs are universally disallowed, we embrace this result and put it in a typological context. We adopt an Optimality Theory constraint set to model SIVs (Kager and Martínez-Paricio 2018b), and extend it, paving the way for a typological investigation of SIVs.
In this paper, we identify a new type of ternarity found in bounded tone spreading in Copperbelt Bemba (Bickmore & Kula 2013).We argue that this ternarity must be metrical in nature, because it is quantity-sensitive and therefore not capturable in a straightforward counting rule.Traditional binary feet approaches to ternarity hinge on the minimal presence of unparsed syllables.However, ternarity in Copperbelt Bemba can occur in the presence of a multitude of unparsed syllables.Consequently, we argue that Copperbelt Bemba demands a larger foot constituent.We apply layered feet (Martínez-Paricio & Kager, 2013) to the analysis of the pattern, proposing that the foot in Copperbelt Bemba has an inner iamb and a monomoraic adjunct.The data is complicated by cases of falling tones on heavy syllables. We propose that these are reflections of syllable integrity violating feet, and point to other cases where this representational device has been used. Thus, we identify Copperbelt Bemba as the first instance of a blended foot, which allows more syllable integrity violations than syllabic feet, but fewer than moraic feet.
The paper models the acquisition of quantity insensitive metrical stress through constraint induction. A single constraint format is specified that regulates the alignment of prosodic categories. A binary and ternary foot-based prosodic hierarchy are compared in their conduciveness to learning a range of stress patterns, with clear advantages for the latter. The paper also points out the interaction between grammatical modeling and acquisition modeling with regards to the typological predictions of the grammar formalization.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.