scholarly journals Modeling the Duration of Word-Final S in English with Naive Discriminative Learning

2019 ◽  
Author(s):  
Fabian Tomaschek ◽  
Ingo Plag ◽  
Mirjam Ernestus ◽  
R. H. Baayen
Keyword(s):  
Lexical Processing ◽  
Discriminative Learning ◽  
Prosodic Structure ◽  
Learning Network ◽  
Theoretical Morphology ◽  
English Vowels ◽  
Third Person ◽  
Segment Duration

Recent research on the acoustic realization of affixes has revealed differencesbetween phonologically homophonous affixes, for example the different kinds offinal [s] and [z] in English (Plag et al. 2017, Zimmermann 2016). Such resultsare unexpected and unaccounted for in widely-accepted post-Bloomfieldian item-and-arrangement models (Hockett, 1954), which separate lexical and post-lexicalphonology, and in models which interpret phonetic effects as consequences of different prosodic structure. This paper demonstrates that the differences in duration of English final S as a function of the morphological function it expresses (non-morphemic, plural, third person singular, genitive, genitive plural, cliticized has, and cliticized is) can be approximated by considering the support for these morphological functions from the words’ sublexical and collocational properties. We estimated this support using naive discriminative learning, and replicated previous results for English vowels (Tucker et al., 2019) indicating that segment duration is lengthened under higher functional certainty, but shortened under functional uncertainty. We discuss the implications of these results, obtained with wide learning network that eschews representations for morphemes and exponents, for models in theoretical morphology as well as for models of lexical processing.

scholarly journals Phonetic effects of morphology and context: Modeling the duration of word-final S in English with naïve discriminative learning

2019 ◽  
pp. 1-39 ◽  
Author(s):  
FABIAN TOMASCHEK ◽  
INGO PLAG ◽  
MIRJAM ERNESTUS ◽  
R. HARALD BAAYEN
Keyword(s):  
Lexical Processing ◽  
Discriminative Learning ◽  
Prosodic Structure ◽  
Context Modeling ◽  
Learning Network ◽  
Theoretical Morphology ◽  
English Vowels ◽  
Third Person ◽  
Lexical Phonology ◽  
Segment Duration

Recent research on the acoustic realization of affixes has revealed differences between phonologically homophonous affixes, e.g. the different kinds of final [s] and [z] in English (Plag, Homann & Kunter 2017, Zimmermann 2016a). Such results are unexpected and unaccounted for in widely accepted post-Bloomfieldian item-and-arrangement models (Hockett 1954), which separate lexical and post-lexical phonology, and in models which interpret phonetic effects as consequences of different prosodic structure. This paper demonstrates that the differences in duration of English final S as a function of the morphological function it expresses (non-morphemic, plural, third person singular, genitive, genitive plural, cliticizedhas, and cliticizedis) can be approximated by considering the support for these morphological functions from the words’ sublexical and collocational properties. We estimated this support using naïve discriminative learning and replicated previous results for English vowels (Tucker, Sims & Baayen 2019), indicating that segment duration is lengthened under higher functional certainty but shortened under functional uncertainty. We discuss the implications of these results, obtained with a wide learning network that eschews representations for morphemes and exponents, for models in theoretical morphology as well as for models of lexical processing.

Discriminative Learning and the Lexicon: NDL and LDL

2021 ◽  
Author(s):  
Yu-Ying Chuang ◽  
R. Harald Baayen
Keyword(s):  
Lexical Processing ◽  
Morphological Processing ◽  
Discriminative Learning ◽  
Computational Algorithms ◽  
Association Strength ◽  
Computational Theory ◽  
Wide Range ◽  
Multivariate Multiple Regression ◽  
Lexical Learning ◽  
Set Up

Naive discriminative learning (NDL) and linear discriminative learning (LDL) are simple computational algorithms for lexical learning and lexical processing. Both NDL and LDL assume that learning is discriminative, driven by prediction error, and that it is this error that calibrates the association strength between input and output representations. Both words’ forms and their meanings are represented by numeric vectors, and mappings between forms and meanings are set up. For comprehension, form vectors predict meaning vectors. For production, meaning vectors map onto form vectors. These mappings can be learned incrementally, approximating how children learn the words of their language. Alternatively, optimal mappings representing the end state of learning can be estimated. The NDL and LDL algorithms are incorporated in a computational theory of the mental lexicon, the ‘discriminative lexicon’. The model shows good performance both with respect to production and comprehension accuracy, and for predicting aspects of lexical processing, including morphological processing, across a wide range of experiments. Since, mathematically, NDL and LDL implement multivariate multiple regression, the ‘discriminative lexicon’ provides a cognitively motivated statistical modeling approach to lexical processing.

scholarly journals Durational Differences of Word-Final /s/ Emerge From the Lexicon: Modelling Morpho-Phonetic Effects in Pseudowords With Linear Discriminative Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Dominic Schmitz ◽  
Ingo Plag ◽  
Dinah Baer-Henney ◽  
Simon David Stein
Keyword(s):  
Speech Production ◽  
Discriminative Learning ◽  
Production Data ◽  
Real Word ◽  
Theoretical Level ◽  
Learning Network ◽  
Recent Approach ◽  
Production Study ◽  
Different Types

Recent research has shown that seemingly identical suffixes such as word-final /s/ in English show systematic differences in their phonetic realisations. Most recently, durational differences between different types of /s/ have been found to also hold for pseudowords: the duration of /s/ is longest in non-morphemic contexts, shorter with suffixes, and shortest in clitics. At the theoretical level such systematic differences are unexpected and unaccounted for in current theories of speech production. Following a recent approach, we implemented a linear discriminative learning network trained on real word data in order to predict the duration of word-final non-morphemic and plural /s/ in pseudowords using production data by a previous production study. It is demonstrated that the duration of word-final /s/ in pseudowords can be predicted by LDL networks trained on real word data. That is, duration of word-final /s/ in pseudowords can be predicted based on their relations to the lexicon.

scholarly journals Morpho-Phonetic Effects in Speech Production: Modeling the Acoustic Duration of English Derived Words With Linear Discriminative Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Simon David Stein ◽  
Ingo Plag
Keyword(s):  
Speech Production ◽  
Morphological Structure ◽  
Computational Approach ◽  
Natural Speech ◽  
Discriminative Learning ◽  
Promising Alternative ◽  
Learning Network ◽  
Speech Data ◽  
Production Modeling

Recent evidence for the influence of morphological structure on the phonetic output goes unexplained by established models of speech production and by theories of the morphology-phonology interaction. Linear discriminative learning (LDL) is a recent computational approach in which such effects can be expected. We predict the acoustic duration of 4,530 English derivative tokens with the morphological functions DIS, NESS, LESS, ATION, and IZE in natural speech data by using predictors derived from a linear discriminative learning network. We find that the network is accurate in learning speech production and comprehension, and that the measures derived from it are successful in predicting duration. For example, words are lengthened when the semantic support of the word's predicted articulatory path is stronger. Importantly, differences between morphological categories emerge naturally from the network, even when no morphological information is provided. The results imply that morphological effects on duration can be explained without postulating theoretical units like the morpheme, and they provide further evidence that LDL is a promising alternative for modeling speech production.

scholarly journals On the processing of nonwords in word naming and auditory lexical decision

2020 ◽  
Author(s):  
Yu-Ying Chuang ◽  
Marie-lenka Voller ◽  
Elnaz Shafaei-Bajestan ◽  
Susanne Gahl ◽  
Peter Hendrix ◽  
...  
Keyword(s):  
Lexical Decision ◽  
Speech Signal ◽  
Lexical Processing ◽  
Semantic Space ◽  
Word Naming ◽  
Model Fit ◽  
Discriminative Learning ◽  
Auditory Lexical Decision ◽  
Improve Model

Nonwords are often used to clarify how lexical processing takes place in the absence of semantics. This study shows that nonwords are not semantically vacuous. We used Linear Discriminative Learning (Baayen et al., 2019) to estimate the meanings of nonwords in the MALD database (Tucker et al., 2018) from the speech signal. We show that measures gauging nonword semantics significantly improve model fit for both acoustic durations and RTs. Although nonwords do not evoke meanings that afford conscious reflexion, they do make contact with the semantic space, and the angles and distances of nonwords with respect to actual words co-determine articulation and lexicality decisions.

scholarly journals The Discriminative Lexicon: A Unified Computational Model for the Lexicon and Lexical Processing in Comprehension and Production Grounded Not in (De)Composition but in Linear Discriminative Learning

Complexity ◽  
2019 ◽  
Vol 2019 ◽  
pp. 1-39 ◽  
Author(s):  
R. Harald Baayen ◽  
Yu-Ying Chuang ◽  
Elnaz Shafaei-Bajestan ◽  
James P. Blevins
Keyword(s):  
Machine Learning ◽  
Computational Model ◽  
Lexical Processing ◽  
Discriminative Learning ◽  
Auditory Comprehension ◽  
Linear Networks ◽  
Novel Theory ◽  
Novel Complex ◽  
Complex Words ◽  
Novel Model

The discriminative lexicon is introduced as a mathematical and computational model of the mental lexicon. This novel theory is inspired by word and paradigm morphology but operationalizes the concept of proportional analogy using the mathematics of linear algebra. It embraces the discriminative perspective on language, rejecting the idea that words’ meanings are compositional in the sense of Frege and Russell and arguing instead that the relation between form and meaning is fundamentally discriminative. The discriminative lexicon also incorporates the insight from machine learning that end-to-end modeling is much more effective than working with a cascade of models targeting individual subtasks. The computational engine at the heart of the discriminative lexicon is linear discriminative learning: simple linear networks are used for mapping form onto meaning and meaning onto form, without requiring the hierarchies of post-Bloomfieldian ‘hidden’ constructs such as phonemes, morphemes, and stems. We show that this novel model meets the criteria of accuracy (it properly recognizes words and produces words correctly), productivity (the model is remarkably successful in understanding and producing novel complex words), and predictivity (it correctly predicts a wide array of experimental phenomena in lexical processing). The discriminative lexicon does not make use of static representations that are stored in memory and that have to be accessed in comprehension and production. It replaces static representations by states of the cognitive system that arise dynamically as a consequence of external or internal stimuli. The discriminative lexicon brings together visual and auditory comprehension as well as speech production into an integrated dynamic system of coupled linear networks.

scholarly journals The processing of pseudoword form and meaning in production and comprehension: A computational modeling approach using Linear Discriminative Learning

2020 ◽  
Author(s):  
Yu-Ying Chuang ◽  
Marie-lenka Voller ◽  
Elnaz Shafaei-Bajestan ◽  
Susanne Gahl ◽  
Peter Hendrix ◽  
...  
Keyword(s):  
Lexical Decision ◽  
Lexical Processing ◽  
Cognitive Model ◽  
Reaction Times ◽  
Discriminative Learning ◽  
Common Assumption ◽  
Auditory Lexical Decision ◽  
Model Predictions ◽  
Morphological Theory ◽  
Shed Light

Pseudowords have long served as key tools in psycholinguistic investigations of the lexicon. A common assumption underlying the use of pseudowords is that they are devoid of meaning: Comparing words and pseudowords may then shed light on how meaningful linguistic elements are processed differently from meaningless sound strings.However, pseudowords may in fact carry meaning. On the basis of a computational model of lexical processing, Linear Discriminative Learning (LDL Baayen et al., 2019), we compute numeric vectors representing the semantics of pseudowords. We demonstrate that quantitative measures gauging the semantic neighborhoods of pseudowords predict reaction times in the Massive Auditory Lexical Decision (MALD) database (Tucker et al., 2018). We also show that the model successfully predicts the acoustic durations of pseudowords. Importantly, model predictions hinge on the hypothesis that the mechanisms underlying speech production and comprehension interact. Thus, pseudowords emerge as an outstanding tool for gauging the resonance between production and comprehension.Many pseudowords in the MALD database contain inflectional suffixes. Unlike many contemporary models, LDL captures the semantic commonalities of forms sharing inflectional exponents without using the linguistic construct of morphemes. We discuss methodological and theoretical implications for models of lexical processing and morphological theory. The results of this study, complementing those on real words reported in Baayen et al. (2019), thus provide further evidence for the usefulness of LDL both as a cognitive model of the mental lexicon, and as a tool for generating new quantitative measures that are predictive for human lexical processing.

scholarly journals Glottal stops do not constrain lexical access as do oral stops

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0259573
Author(s):  
Holger Mitterer ◽  
Sahyang Kim ◽  
Taehong Cho
Keyword(s):  
Lexical Access ◽  
Lexical Processing ◽  
Prosodic Structure ◽  
Processing Stage ◽  
Acoustic Mismatch ◽  
Glottal Stop ◽  
Initial Forms

This study explores processing characteristics of a glottal stop in Maltese which occurs both as a phoneme and as an epenthetic stop for vowel-initial words. Experiment 1 shows that its hyperarticulation is not necessarily mapped onto an underlying form, although listeners may interpret it as underlying at a later processing stage. Experiment 2 shows that listeners’ experience with a particular speaker’s use of a glottal stop exclusively as a phoneme does not modulate competition patterns accordingly. Not only are vowel-initial words activated by [ʔ]-initial forms, but /ʔ/-initial words are also activated by vowel-initial forms, suggesting that lexical access is not constrained by an initial acoustic mismatch that involves a glottal stop. Experiment 3 reveals that the observed pattern is not generalizable to an oral stop /t/. We propose that glottal stops have a special status in lexical processing: it is prosodic in nature to be licensed by the prosodic structure.

Second language learners develop non-native lexical processing biases

2019 ◽  
Vol 23 (1) ◽  
pp. 119-130 ◽  
Author(s):  
Seth Wiener
Keyword(s):  
Second Language ◽  
Mandarin Chinese ◽  
Language Learners ◽  
Second Language Learners ◽  
Lexical Processing ◽  
Native Language ◽  
Spoken Language ◽  
Language Recognition ◽  
English Vowels ◽  
L1 And L2

AbstractInfants develop language-specific biases favoring either consonantal or vocalic information. These phonological biases affect various levels of spoken-language recognition in children and adults. This study explored whether adults who speak a second language (L2) apply phonological biases during L2 lexical processing, and whether the biases applied are those of the native language (L1), or those appropriate for the L2. Two word reconstruction experiments were carried out in English and Mandarin Chinese. L1 and L2 speakers of English demonstrated a consonantal bias by changing English vowels faster than consonants. L1 and L2 speakers of Mandarin demonstrated a vocalic bias by changing Mandarin consonants faster than vowels. Even relatively late L2 classroom learners whose L1 triggers a consonantal bias (English) exhibited a vocalic bias in their L2 (Mandarin). Lexically related processing biases are thus determined by the phonological and lexical characteristics of the stimuli being processed and not solely by listeners’ L1.

Distinctiveness and Assimilation in Vowel Perception in a Second Language

2019 ◽  
Vol 62 (12) ◽  
pp. 4534-4543
Author(s):  
Wei Hu ◽  
Sha Tao ◽  
Mingshuang Li ◽  
Chang Liu
Keyword(s):  
Second Language ◽  
Native Language ◽  
Vowel Identification ◽  
Vowel Perception ◽  
Perceptual Distance ◽  
Vowel Space ◽  
English Vowels ◽  
Chinese Counterpart ◽  
Vowel Categories

Purpose The purpose of this study was to investigate how the distinctive establishment of 2nd language (L2) vowel categories (e.g., how distinctively an L2 vowel is established from nearby L2 vowels and from the native language counterpart in the 1st formant [F1] × 2nd formant [F2] vowel space) affected L2 vowel perception. Method Identification of 12 natural English monophthongs, and categorization and rating of synthetic English vowels /i/ and /ɪ/ in the F1 × F2 space were measured for Chinese-native (CN) and English-native (EN) listeners. CN listeners were also examined with categorization and rating of Chinese vowels in the F1 × F2 space. Results As expected, EN listeners significantly outperformed CN listeners in English vowel identification. Whereas EN listeners showed distinctive establishment of 2 English vowels, CN listeners had multiple patterns of L2 vowel establishment: both, 1, or neither established. Moreover, CN listeners' English vowel perception was significantly related to the perceptual distance between the English vowel and its Chinese counterpart, and the perceptual distance between the adjacent English vowels. Conclusions L2 vowel perception relied on listeners' capacity to distinctively establish L2 vowel categories that were distant from the nearby L2 vowels.

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