#### Filter Results:

#### Publication Year

1993

2016

#### Publication Type

#### Co-author

#### Key Phrase

#### Publication Venue

Learn More

- Paul Boersma, Bruce Hayes
- 1999

The Gradual Learning Algorithm (Boersma 1997) is a constraint-ranking algorithm for learning optimality-theoretic grammars. The purpose of this article is to assess the capabilities of the Gradual Learning Algorithm, particularly in comparison with the Constraint Demotion initiated the learnability research program for Optimality Theory. We argue that the… (More)

- Paul Boersma
- 1997

Variation is controlled by the grammar, though indirectly: it follows automatically from the robustness requirement of learning. If every constraint in an Optimality-Theoretic grammar has a ranking value along a continuous scale, and the disharmony of a constraint at evaluation time is randomly distributed about this value, the phenomenon of optionality in… (More)

- Paul Boersma
- 1993

We present a straightforward and robust algorithm for periodicity detection, working in the lag (autocorrelation) domain. When it is tested for periodic signals and for signals with additive noise or jitter, it proves to be several orders of magnitude more accurate than the methods commonly used for speech analysis. This makes our method capable of… (More)

- Paul Boersma, Joe Pater
- 2008

This paper investigates a gradual on-line learning algorithm for Harmonic Grammar. By adapting existing convergence proofs for perceptrons, we show that for any nonvarying target language, Harmonic-Grammar learners are guaranteed to converge to an appropriate grammar, if they receive complete information about the structure of the learning data. We also… (More)

- Paul Boersma
- 2008

This paper shows that Error-Driven Constraint Demotion (EDCD), an error-driven learning algorithm proposed by Tesar (1995) for Prince and Smolensky's (1993) version of Optimality Theory, can fail to converge to a totally ranked hierarchy of constraints, unlike the earlier non-error-driven learning algorithms proposed by Tesar and Smolensky (1993). The cause… (More)

- Paul Boersma
- 1999

This tutorial yields a step-by-step introduction to stochastic OT grammars and about how you can use the Gradual Learning Algorithm available in the Praat program to help you rank Optimality-Theoretic constraints in ordinal and stochastic grammars. This tutorial describes how you can draw Optimality-Theoretic tableaus and simulate Optimality-Theoretic… (More)

- Paola Escudero, Paul Boersma, Andréia Schurt Rauber, Ricardo A H Bion
- The Journal of the Acoustical Society of America
- 2009

This paper examines four acoustic correlates of vowel identity in Brazilian Portuguese (BP) and European Portuguese (EP): first formant (F1), second formant (F2), duration, and fundamental frequency (F0). Both varieties of Portuguese display some cross-linguistically common phenomena: vowel-intrinsic duration, vowel-intrinsic pitch, gender-dependent size of… (More)

This paper gives an Optimality-Theoretic formalization of several aspects of the acquisition of phonological perception in a second language. The subject matter will be the acquisition of the Spanish vowel system by Dutch learners of Spanish, as evidenced in a listening experiment. Since an explanation of the learners' acquisition path requires knowledge of… (More)

- Paul Boersma, Bruce Morén, Christian Uffmann, Peter Jurgec, Ove Lorentz, Silvia Blaho +1 other
- 2007

The phonology-phonetics interface can be described in terms of cue constraints. This paper illustrates the workings of cue constraints in interaction with each other and in interaction with other classes of constraints. Beside their general usefulness in describing prelexical perception and phonetic implementation, cue constraints help to account for… (More)

- Paul Boersma, Paola Escudero, Rachel Hayes
- 2003

We introduce a two-stage model for the perceptual acquisition of speech sound categories within the framework of Stochastic Optimality Theory and the Gradual Learning Algorithm [1]. During the first stage, learning of language-specific sound categories by infants is driven by distributional evidence in the linguistic input. This auditory-driven learning… (More)