Phonetics, Phonology, and Morphology

Lung’Ie Word Prosody: One system or two?

Ana Lívia Agostinho
Larry M. Hyman
2021

Creole languages have generally not figured prominently in cross-linguistic studies of word-prosodic typology. In this paper, we present a phonological analysis of the prosodic system of Lung’Ie or Principense (ISO 639-3 code: pre), a Portuguese-lexifier creole language spoken in São Tomé and Príncipe. Lung’Ie has produced a unique result of the contact between the two different prosodic systems common in creolization: a stress-accent lexifier and tone language substrates. The language has a restrictive privative H/Ø tone system, in which the /H/ is culminative, but non-obligatory....

Phonological typology

Larry M. Hyman
Frans Plank
2018

Despite earlier work by Trubetzkoy, Jakobson and Greenberg, phonological typology is often underrepresented in typology textbooks. At the same time, most phonologists do not see a difference between phonological typology and cross-linguistic (formal) phonology. The purpose of this book is to bring together leading scholars to address the issue of phonological typology, both in terms of the unity and the diversity of phonological systems.

The conjoint/disjoint alternation in Bantu

Jenneke van der Wal
Larry M. Hyman
2017

This volume brings together descriptions and analyses of the conjoint/disjoint alternation, a typologically significant phenomenon found in many Bantu languages. The chapters provide in-depth documentation, comparative studies and theoretical analyses of the alternation from a range of Bantu languages, showing its crosslinguistic variation in constituent structure, morphology, prosody and information structure.

Post-nasal devoicing and the blurring process

Gašper Beguš
2019

This paper addresses one of the most contested issues in phonology: unnatural alternations. First, non-natural phonological processes are subdivided into unmotivated and unnatural. The central topic of the paper is an unnatural process: post-nasal devoicing (PND). I collect thirteen cases of PND and argue that in all reported cases, PND does not derive from a single unnatural sound change (as claimed in some individual accounts of the data), but rather from a combination of three sound changes, each of which is phonetically motivated. I present new evidence showing that the three stages...

Estimating historical probabilities of natural and unnatural processes

Gašper Beguš
2021

This paper presents a technique for estimating the influences of channel bias on phonological typology. The technique, based on statistical bootstrapping, enables the estimation of historical probability, the probability that a synchronic alternation arises based on two diachronic factors: the number of sound changes required for an alternation to arise and their respective probabilities. I estimate historical probabilities of six attested and unattested alternations targeting the feature [voice], compare historical probabilities of these alternations, perform inferential statistics...

Modeling unsupervised phonetic and phonological learning in Generative Adversarial Phonology

Gašper Beguš
2019

This paper models phonetic and phonological learning as a dependency between random space and generated speech data in the Generative Adversarial Neural network architecture and proposes a methodology to uncover the network’s internal representation that corresponds to phonetic and phonological features. A Generative Adversarial Network (Goodfellow et al. 2014; implemented as WaveGAN for acoustic data by Donahue et al. 2019) was trained on an allophonic distribution in English, where voiceless stops surface as aspirated word-initially before stressed vowels except if preceded by a sibilant...

Segmental Phonetics and Phonology in Caucasian languages

Gašper Beguš
2021

This chapter surveys the major topics of Caucasian segmental phonetics and phonology, focusing on topics with broader implications for general phonetic and phonological theory. The author first presents an acoustic phonetic analysis of phonemic inventories in the three Caucasian families, including both a review of recent instrumental data on the topic as well as a new analysis of new and existing experimental acoustic data. This analysis focuses on four primary topics: obstruents with different laryngeal features, typologically unusual segments, small vocalic inventories, and...

Identity-Based Patterns in Deep Convolutional Networks: Generative Adversarial Phonology and Reduplication

Gašper Beguš
2021

This paper models unsupervised learning of an identity-based pattern (or copying) in speech called reduplication from raw continuous data with deep convolutional neural networks. We use the ciwGAN architecture (Beguš, 2021a) in which learning of meaningful representations in speech emerges from a requirement that the CNNs generate informative data. We propose a technique to wug-test CNNs trained on speech and, based on four generative tests, argue that the network learns to represent an identity-based pattern in its latent space. By manipulating only two...

Generative Adversarial Phonology: Modeling Unsupervised Phonetic and Phonological Learning With Neural Networks

Gašper Beguš
2020

Training deep neural networks on well-understood dependencies in speech data can provide new insights into how they learn internal representations. This paper argues that acquisition of speech can be modeled as a dependency between random space and generated speech data in the Generative Adversarial Network architecture and proposes a methodology to uncover the network's internal representations that correspond to phonetic and phonological properties. The Generative Adversarial architecture is uniquely appropriate for modeling phonetic and phonological learning because the network is...

Local and non-local dependency learning and emergence of rule-like representations in speech data by deep convolutional generative adversarial networks

Gašper Beguš
2021

This paper argues that training Generative Adversarial Networks (GANs) on local and non-local dependencies in speech data offers insights into how deep neural networks...