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Oratie uitgesproken door Prof.dr. Martijn Wieling op 04 maart 2022 bij de aanvaarding van het ambt van bijzonder hoogleraar Nedersaksische/Groningse taal en cultuur aan de Faculteit der Letteren Rijksuniversiteit Groningen Martijn Wieling, bijzonder hoogleraar Nedersaksische/Groningse taal en cultuur, vertelt in zijn oratie hoe hij en zijn collega’s van het Centrum Groninger Taal en Cultuur (CGTC) digitale technieken gebruiken om variatie en verandering in de Nedersaksische streektaal te onderzoeken. In zijn onderzoek gebruikt Wieling niet alleen bestaande dialectdata, maar verzamelen hij en zijn CGTC-collega’s ook nieuwe dialectdata. Deze data wordt opgeslagen in WoordWaark (www.woordwaark.nl), een online digitale taaldatabank voor het Gronings, opgezet en gecoördineerd door hoogleraar Goffe Jensma en ontwikkeld door dr. Wilbert Heeringa. Een van de projecten waarmee Wieling nieuwe dialectdata verzamelt is het door dr. Nanna Hilton geïnitieerde “Stemmen”-project. Deze website voorspelt waar een spreker vandaan komt op basis van zijn of haar dialectuitspraken. Van old noar jong Een tweede belangrijk project is “Van old noar jong”. In dit door Wieling gecoördineerde project wordt een onderwijsgame ontwikkeld waarmee kinderen op een toegankelijke manier kennis kunnen maken met het lokale Groningse dialect. Dit project wordt ondersteund door een Google Community Grant van 30.000 euro en is opgezet in samenwerking met hoogleraar Jensma en Goos Gosling Slotegraaf van Dorpsbelangen Zandeweer, Eppenhuizen en Doodstil. Een derde project waar Wieling zich voor inzet is de ontwikkeling van een Gronings tekst-naar-spraak systeem. In samenwerking met onder anderen Jenny van Doorn, RUG-hoogleraar dienstenmarketing, wordt onderzocht of zo’n systeem inzetbaar is in bijvoorbeeld de zorg. Een aanvullend doel van dit systeem is dat hiermee op termijn alle Groningse teksten binnen WoordWaark automatisch voorgelezen zullen kunnen worden

We introduce the Dutch Model Benchmark: DUMB. The benchmark includes a diverse set of datasets for low-, medium- and high-resource tasks. The total set of nine tasks includes four tasks that were previously not available in Dutch. Instead of relying on a mean score across tasks, we propose Relative Error Reduction (RER), which compares the DUMB performance of language models to a strong baseline which can be referred to in the future even when assessing different sets of language models. Through a comparison of 14 pre-trained language models (mono- and multi-lingual, of varying sizes), we assess the internal consistency of the benchmark tasks, as well as the factors that likely enable high performance. Our results indicate that current Dutch monolingual models under-perform and suggest training larger Dutch models with other architectures and pre-training objectives. At present, the highest performance is achieved by DeBERTaV3 (large), XLM-R (large) and mDeBERTaV3 (base). In addition to highlighting best strategies for training larger Dutch models, DUMB will foster further research on Dutch. A public leaderboard is available at https://dumbench.nl., Comment: EMNLP 2023 camera-ready

The performance of automatic speech recognition (ASR) systems has advanced substantially in recent years, particularly for languages for which a large amount of transcribed speech is available. Unfortunately, for low-resource languages, such as minority languages, regional languages or dialects, ASR performance generally remains much lower. In this study, we investigate whether data augmentation techniques could help improve low-resource ASR performance, focusing on four typologically diverse minority languages or language variants (West Germanic: Gronings, West-Frisian; Malayo-Polynesian: Besemah, Nasal). For all four languages, we examine the use of self-training, where an ASR system trained with the available human-transcribed data is used to generate transcriptions, which are then combined with the original data to train a new ASR system. For Gronings, for which there was a pre-existing text-to-speech (TTS) system available, we also examined the use of TTS to generate ASR training data from text-only sources. We find that using a self-training approach consistently yields improved performance (a relative WER reduction up to 20.5% compared to using an ASR system trained on 24 minutes of manually transcribed speech). The performance gain from TTS augmentation for Gronings was even stronger (up to 25.5% relative reduction in WER compared to a system based on 24 minutes of manually transcribed speech). In sum, our results show the benefit of using self-training or (if possible) TTS-generated data as an efficient solution to overcome the limitations of data availability for resource-scarce languages in order to improve ASR performance., Comment: Accepted at ACL 2023

Oratie uitgesproken door prof.dr. Martijn Wieling op 04 maart 2022 bij de aanvaarding van het ambt van bijzonder hoogleraar Nedersaksische/Groningse taal en cultuur aan de Faculteit der Letteren Rijksuniversiteit Groningen Martijn Wieling, bijzonder hoogleraar Nedersaksische/Groningse taal en cultuur, vertelt in zijn oratie hoe hij en zijn collega’s van het Centrum Groninger Taal en Cultuur (CGTC) digitale technieken gebruiken om variatie en verandering in de Nedersaksische streektaal te onderzoeken. In zijn onderzoek gebruikt Wieling niet alleen bestaande dialectdata, maar verzamelen hij en zijn CGTC-collega’s ook nieuwe dialectdata. Deze data wordt opgeslagen in WoordWaark (www.woordwaark.nl), een online digitale taaldatabank voor het Gronings, opgezet en gecoördineerd door hoogleraar Goffe Jensma en ontwikkeld door dr. Wilbert Heeringa. Een van de projecten waarmee Wieling nieuwe dialectdata verzamelt is het door dr. Nanna Hilton geïnitieerde “Stemmen”-project. Deze website voorspelt waar een spreker vandaan komt op basis van zijn of haar dialectuitspraken. Van old noar jong Een tweede belangrijk project is “Van old noar jong”. In dit door Wieling gecoördineerde project wordt een onderwijsgame ontwikkeld waarmee kinderen op een toegankelijke manier kennis kunnen maken met het lokale Groningse dialect. Dit project wordt ondersteund door een Google Community Grant van 30.000 euro en is opgezet in samenwerking met hoogleraar Jensma en Goos Gosling Slotegraaf van Dorpsbelangen Zandeweer, Eppenhuizen en Doodstil. Een derde project waar Wieling zich voor inzet is de ontwikkeling van een Gronings tekst-naar-spraak systeem. In samenwerking met onder anderen Jenny van Doorn, RUG-hoogleraar dienstenmarketing, wordt onderzocht of zo’n systeem inzetbaar is in bijvoorbeeld de zorg. Een aanvullend doel van dit systeem is dat hiermee op termijn alle Groningse teksten binnen WoordWaark automatisch voorgelezen zullen kunnen worden. Omslagontwerp en opmaak: LINE UP boek en media b

This document outlines a PROSPERO pre-registered protocol for a systematic review regarding articulatory changes in speech following oral or orophayrngeal cancer treatment. Treatment of tumours in the oral cavity may result in physiological changes that could lead to articulatory difficulties. The tongue becomes less mobile due to scar tissue and/or potential (postoperative) radiation therapy. Moreover, tissue loss may create a bypass for airflow or limit constriction possibilities. In order to gain a better understanding of the nature of the speech problems, information regarding the movement of the articulators is needed since perceptual or acoustic information provide only indirect evidence of articulatory changes. Therefore, this systematic review will review studies that directly measured the articulatory movements of the tongue, jaw, and lips following treatment for oral or oropharyngeal cancer.

This article reports investigations into sound change at the community-level of Frisian and Low Saxon dialect groups in the north of the Netherlands, which differ in key factors influencing dialect decline. We combine phonetically transcribed corpora with dialectometric approaches that can quantify change among older male dialect speakers in a real-time framework. A multidimensional variant of the Levenshtein distance, combined with methods that induce realistic distances between sounds, is used to estimate how much dialect groups converged to and diverged from Standard Dutch between 1990 and 2010. Our analyses indicate that sound change is a slow process in this geographical area. The Frisian and North Low Saxon dialect groups seem to be most stable, while Westphalian Low Saxon varieties seem to be most prone to change. We offer possible explanations for our findings and discuss shortcomings of the data and approach in detail.

Oratie uitgesproken door Prof.dr. Martijn Wieling op 04 maart 2022 bij de aanvaarding van het ambt van bijzonder hoogleraar Nedersaksische/Groningse taal en cultuur aan de Faculteit der Letteren Rijksuniversiteit Groningen Martijn Wieling, bijzonder hoogleraar Nedersaksische/Groningse taal en cultuur, vertelt in zijn oratie hoe hij en zijn collega’s van het Centrum Groninger Taal en Cultuur (CGTC) digitale technieken gebruiken om variatie en verandering in de Nedersaksische streektaal te onderzoeken. In zijn onderzoek gebruikt Wieling niet alleen bestaande dialectdata, maar verzamelen hij en zijn CGTC-collega’s ook nieuwe dialectdata. Deze data wordt opgeslagen in WoordWaark (www.woordwaark.nl), een online digitale taaldatabank voor het Gronings, opgezet en gecoördineerd door hoogleraar Goffe Jensma en ontwikkeld door dr. Wilbert Heeringa. Een van de projecten waarmee Wieling nieuwe dialectdata verzamelt is het door dr. Nanna Hilton geïnitieerde “Stemmen”-project. Deze website voorspelt waar een spreker vandaan komt op basis van zijn of haar dialectuitspraken. Van old noar jong Een tweede belangrijk project is “Van old noar jong”. In dit door Wieling gecoördineerde project wordt een onderwijsgame ontwikkeld waarmee kinderen op een toegankelijke manier kennis kunnen maken met het lokale Groningse dialect. Dit project wordt ondersteund door een Google Community Grant van 30.000 euro en is opgezet in samenwerking met hoogleraar Jensma en Goos Gosling Slotegraaf van Dorpsbelangen Zandeweer, Eppenhuizen en Doodstil. Een derde project waar Wieling zich voor inzet is de ontwikkeling van een Gronings tekst-naar-spraak systeem. In samenwerking met onder anderen Jenny van Doorn, RUG-hoogleraar dienstenmarketing, wordt onderzocht of zo’n systeem inzetbaar is in bijvoorbeeld de zorg. Een aanvullend doel van dit systeem is dat hiermee op termijn alle Groningse teksten binnen WoordWaark automatisch voorgelezen zullen kunnen worden

Deep acoustic models represent linguistic information based on massive amounts of data. Unfortunately, for regional languages and dialects such resources are mostly not available. However, deep acoustic models might have learned linguistic information that transfers to low-resource languages. In this study, we evaluate whether this is the case through the task of distinguishing low-resource (Dutch) regional varieties. By extracting embeddings from the hidden layers of various wav2vec 2.0 models (including new models which are pre-trained and/or fine-tuned on Dutch) and using dynamic time warping, we compute pairwise pronunciation differences averaged over 10 words for over 100 individual dialects from four (regional) languages. We then cluster the resulting difference matrix in four groups and compare these to a gold standard, and a partitioning on the basis of comparing phonetic transcriptions. Our results show that acoustic models outperform the (traditional) transcription-based approach without requiring phonetic transcriptions, with the best performance achieved by the multilingual XLSR-53 model fine-tuned on Dutch. On the basis of only six seconds of speech, the resulting clustering closely matches the gold standard., Comment: Accepted at NAACL 2022

We present an articulatory synthesis framework for the synthesis and manipulation of oral cancer speech for clinical decision making and alleviation of patient stress. Objective and subjective evaluations demonstrate that the framework has acceptable naturalness and is worth further investigation. A subsequent subjective vowel and consonant identification experiment showed that the articulatory synthesis system can manipulate the articulatory trajectories so that the synthesised speech reproduces problems present in the ground truth oral cancer speech., Comment: 5 pages, 4 tables, 1 figure. Submitted to Interspeech 2022

Oratie uitgesproken door prof.dr. Martijn Wieling op 04 maart 2022 bij de aanvaarding van het ambt van bijzonder hoogleraar Nedersaksische/Groningse taal en cultuur aan de Faculteit der Letteren Rijksuniversiteit Groningen Martijn Wieling, bijzonder hoogleraar Nedersaksische/Groningse taal en cultuur, vertelt in zijn oratie hoe hij en zijn collega’s van het Centrum Groninger Taal en Cultuur (CGTC) digitale technieken gebruiken om variatie en verandering in de Nedersaksische streektaal te onderzoeken. In zijn onderzoek gebruikt Wieling niet alleen bestaande dialectdata, maar verzamelen hij en zijn CGTC-collega’s ook nieuwe dialectdata. Deze data wordt opgeslagen in WoordWaark (www.woordwaark.nl), een online digitale taaldatabank voor het Gronings, opgezet en gecoördineerd door hoogleraar Goffe Jensma en ontwikkeld door dr. Wilbert Heeringa. Een van de projecten waarmee Wieling nieuwe dialectdata verzamelt is het door dr. Nanna Hilton geïnitieerde “Stemmen”-project. Deze website voorspelt waar een spreker vandaan komt op basis van zijn of haar dialectuitspraken. Van old noar jong Een tweede belangrijk project is “Van old noar jong”. In dit door Wieling gecoördineerde project wordt een onderwijsgame ontwikkeld waarmee kinderen op een toegankelijke manier kennis kunnen maken met het lokale Groningse dialect. Dit project wordt ondersteund door een Google Community Grant van 30.000 euro en is opgezet in samenwerking met hoogleraar Jensma en Goos Gosling Slotegraaf van Dorpsbelangen Zandeweer, Eppenhuizen en Doodstil. Een derde project waar Wieling zich voor inzet is de ontwikkeling van een Gronings tekst-naar-spraak systeem. In samenwerking met onder anderen Jenny van Doorn, RUG-hoogleraar dienstenmarketing, wordt onderzocht of zo’n systeem inzetbaar is in bijvoorbeeld de zorg. Een aanvullend doel van dit systeem is dat hiermee op termijn alle Groningse teksten binnen WoordWaark automatisch voorgelezen zullen kunnen worden. Omslagontwerp en opmaak: LINE UP boek en media b

For many (minority) languages, the resources needed to train large models are not available. We investigate the performance of zero-shot transfer learning with as little data as possible, and the influence of language similarity in this process. We retrain the lexical layers of four BERT-based models using data from two low-resource target language varieties, while the Transformer layers are independently fine-tuned on a POS-tagging task in the model's source language. By combining the new lexical layers and fine-tuned Transformer layers, we achieve high task performance for both target languages. With high language similarity, 10MB of data appears sufficient to achieve substantial monolingual transfer performance. Monolingual BERT-based models generally achieve higher downstream task performance after retraining the lexical layer than multilingual BERT, even when the target language is included in the multilingual model.

This chapter provides an overview of computational work in dialectology. This chapter is written for computational linguists, so that we will focus less on the technical details of exploiting the computer in research on dialects (which is documented in the articles we cite) but rather more on background assumptions and emerging issues and opportunities.

This paper provides an overview of computational work in dialectology. We have published similar surveys in the not-too-distant past (Heeringa and Prokic, 2018; Wieling and Nerbonne, 2015), but these were aimed at dialectologists and general linguists, respectively. This article is aimed at computational linguists, so that we will focus less on the nuts and bolts of exploiting the computer in research on dialects (which is documented in the articles we cite) and more on background assumptions and emerging issues and opportunities.

An important objective of research in Second Language Acquisition has been to find a simple and reliable way to quantify second language use. Corpora have provided a crucial source of information for these studies. In spite of many attempts to determine an optimal “yardstick” to measure the quality of second language use, particularly lexical complexity and syntactic complexity, a reliable and widely applicable instrument has not yet been determined. The difficulty in finding a suitable instrument can be accounted for in a complex dynamic systems theory (CDST) approach to second language development. An important starting point of this approach is that every next step in time “is the emergent product of context and history, and no component has causal priority” (Thelen 2005, p. 271). In this paper we illustrate this by using a dense longitudinal corpus of the development of 22 highly similar L2 learners, consisting of 23 weekly measurements. The analysis of these data show convincingly that L2 development is a highly individually owned and nonlinear process. While complexity yardsticks (like MLTU and Guiraud) may seem functional from a group perspective, their application to individual learners is very limited.

This chapter provides an overview of computational work in dialectology. This chapter is written for computational linguists, so that we will focus less on the technical details of exploiting the computer in research on dialects (which is documented in the articles we cite) but rather more on background assumptions and emerging issues and opportunities.

For many (minority) languages, the resources needed to train large models are not available. We investigate the performance of zero-shot transfer learning with as little data as possible, and the influence of language similarity in this process. We retrain the lexical layers of four BERT-based models using data from two low-resource target language varieties, while the Transformer layers are independently fine-tuned on a POS-tagging task in the model's source language. By combining the new lexical layers and fine-tuned Transformer layers, we achieve high task performance for both target languages. With high language similarity, 10MB of data appears sufficient to achieve substantial monolingual transfer performance. Monolingual BERT-based models generally achieve higher downstream task performance after retraining the lexical layer than multilingual BERT, even when the target language is included in the multilingual model.

This paper provides an overview of computational work in dialectology. We have published similar surveys in the not-too-distant past (Heeringa and Prokic, 2018; Wieling and Nerbonne, 2015), but these were aimed at dialectologists and general linguists, respectively. This article is aimed at computational linguists, so that we will focus less on the nuts and bolts of exploiting the computer in research on dialects (which is documented in the articles we cite) and more on background assumptions and emerging issues and opportunities.

An important objective of research in Second Language Acquisition has been to find a simple and reliable way to quantify second language use. Corpora have provided a crucial source of information for these studies. In spite of many attempts to determine an optimal “yardstick” to measure the quality of second language use, particularly lexical complexity and syntactic complexity, a reliable and widely applicable instrument has not yet been determined. The difficulty in finding a suitable instrument can be accounted for in a complex dynamic systems theory (CDST) approach to second language development. An important starting point of this approach is that every next step in time “is the emergent product of context and history, and no component has causal priority” (Thelen 2005, p. 271). In this paper we illustrate this by using a dense longitudinal corpus of the development of 22 highly similar L2 learners, consisting of 23 weekly measurements. The analysis of these data show convincingly that L2 development is a highly individually owned and nonlinear process. While complexity yardsticks (like MLTU and Guiraud) may seem functional from a group perspective, their application to individual learners is very limited.

This article reports ongoing investigations into phonetic change of dialect groups in the northern Netherlandic language area, particularly the Frisian and Low Saxon dialect groups, which are known to differ in vitality. To achieve this, we combine existing phonetically transcribed corpora with dialectometric approaches that allow us to quantify change among older male dialect speakers in a real-time framework. A multidimensional variant of the Levenshtein distance, combined with methods that induce realistic phonetic distances between transcriptions, is used to estimate how much dialect groups have changed between 1990 and 2010, and whether they changed towards Standard Dutch or away from it. Our analyses indicate that language change is a slow process in this geographical area. Moreover, the Frisian and Groningen dialect groups seem to be most stable, while the other Low Saxon varieties (excluding the Groningen dialect group) were shown to be most prone to change. We offer possible explanations for our findings, while we discuss shortcomings of the data and approach in detail, as well as desiderata for future research., Comment: Submitted to Taal & Tongval

For many (minority) languages, the resources needed to train large models are not available. We investigate the performance of zero-shot transfer learning with as little data as possible, and the influence of language similarity in this process. We retrain the lexical layers of four BERT-based models using data from two low-resource target language varieties, while the Transformer layers are independently fine-tuned on a POS-tagging task in the model's source language. By combining the new lexical layers and fine-tuned Transformer layers, we achieve high task performance for both target languages. With high language similarity, 10MB of data appears sufficient to achieve substantial monolingual transfer performance. Monolingual BERT-based models generally achieve higher downstream task performance after retraining the lexical layer than multilingual BERT, even when the target language is included in the multilingual model., Comment: Findings of ACL 2021 Camera Ready

In this study, we investigate which factors influence the linguistic distance of Catalan dialectal pronunciations from standard Catalan. We use pronunciations from three regions where the northwestern variety of the Catalan language is spoken (Catalonia, Aragon and Andorra). In contrast to Aragon, Catalan has an official status in both Catalonia and Andorra, which likely influences standardization. Because we are interested in the potentially large range of differences that standardization might promote, we examine 357 words in Catalan varieties and in particular their pronunciation distances with respect to the standard. In order to be sensitive to differences among the words, we fitted a generalized additive mixed-effects regression model to this data. This allows us to examine simultaneously the general (i.e. aggregate) patterns in pronunciation distance and to detect those words that diverge substantially from the general pattern. The results revealed higher pronunciation distances from standard Catalan in Aragon than in the other regions. Furthermore, speakers in Catalonia and Andorra, but not in Aragon, showed a clear standardization pattern, with younger speakers having dialectal pronunciations closer to the standard than older speakers. This clearly indicates the presence of a border effect within a single country with respect to word pronunciation distances. Since a great deal of scholarship focuses on single segment changes, we compare our analysis to the analysis of three segment changes that have been discussed in the literature on Catalan. This comparison revealed that the pattern observed at the word pronunciation level was supported by two of the three cases examined. As not all individual cases conform to the general pattern, t

In this paper we discuss the implications of using machine learning for judicial decision-making in situations where human rights may be infringed. We argue that the use of such tools in these situations should be limited due to inherent status quo bias and dangers of reverse-engineering. We discuss that these issues already exist in the judicial systems without using machine learning tools, but how introducing them might exacerbate them.

Variation in speech is often quantified by comparing phonetic transcriptions of the same utterance. However, manually transcribing speech is time-consuming and error prone. As an alternative, therefore, we investigate the extraction of acoustic embeddings from several self-supervised neural models. We use these representations to compute word-based pronunciation differences between non-native and native speakers of English, and between Norwegian dialect speakers. For comparison with several earlier studies, we evaluate how well these differences match human perception by comparing them with available human judgements of similarity. We show that speech representations extracted from a specific type of neural model (i.e. Transformers) lead to a better match with human perception than two earlier approaches on the basis of phonetic transcriptions and MFCC-based acoustic features. We furthermore find that features from the neural models can generally best be extracted from one of the middle hidden layers than from the final layer. We also demonstrate that neural speech representations not only capture segmental differences, but also intonational and durational differences that cannot adequately be represented by a set of discrete symbols used in phonetic transcriptions., Comment: Submitted to Journal of Phonetics

This chapter presents methods for the analysis of large aggregates and techniques that seek to identify groups together with their common speech habits. Jean Seguy examined the distribution of aggregate differences (in Gascogne) as a function of geography, displaying a sub‐linear curve, which John Nerbonne argues contradicts Peter Trudgill's gravity theory of dialect divergence. These early works motivate the aggregate perspective. Regression analyses have played a role in characterizing the relation between geographic distance and aggregate linguistic differences. Another approach, generalized additive modeling (GAM), is able to simultaneously detect the aggregate geographical pattern, whereas also identifying the importance of other relevant social and lexical predictors. The advantage of the GAM is that it allows one to directly incorporate the complex influence of geography on the aggregate patterns, whereas simultaneously considering the importance of other social and lexical variables.

Given (i) the rise of a new paradigm to machine translation based on neural networks that results in more fluent and less literal output than previous models and (ii) the maturity of machine-assisted translation via post-editing in industry, project PiPeNovel studies the feasibility of the post-editing workflow for literary text conducting experiments with professional literary translators.

Given (i) the rise of a new paradigm to machine translation based on neural networks that results in more fluent and less literal output than previous models and (ii) the maturity of machine-assisted translation via post-editing in industry, project PiPeNovel studies the feasibility of the post-editing workflow for literary text conducting experiments with professional literary translators.

This chapter presents methods for the analysis of large aggregates and techniques that seek to identify groups together with their common speech habits. Jean Seguy examined the distribution of aggregate differences (in Gascogne) as a function of geography, displaying a sub‐linear curve, which John Nerbonne argues contradicts Peter Trudgill's gravity theory of dialect divergence. These early works motivate the aggregate perspective. Regression analyses have played a role in characterizing the relation between geographic distance and aggregate linguistic differences. Another approach, generalized additive modeling (GAM), is able to simultaneously detect the aggregate geographical pattern, whereas also identifying the importance of other relevant social and lexical predictors. The advantage of the GAM is that it allows one to directly incorporate the complex influence of geography on the aggregate patterns, whereas simultaneously considering the importance of other social and lexical variables.

In this study, we investigate which factors influence the linguistic distance of Catalan dialectal pronunciations from standard Catalan. We use pronunciations from three regions where the northwestern variety of the Catalan language is spoken (Catalonia, Aragon and Andorra). In contrast to Aragon, Catalan has an official status in both Catalonia and Andorra, which likely influences standardization. Because we are interested in the potentially large range of differences that standardization might promote, we examine 357 words in Catalan varieties and in particular their pronunciation distances with respect to the standard. In order to be sensitive to differences among the words, we fitted a generalized additive mixed-effects regression model to this data. This allows us to examine simultaneously the general (i.e. aggregate) patterns in pronunciation distance and to detect those words that diverge substantially from the general pattern. The results revealed higher pronunciation distances from standard Catalan in Aragon than in the other regions. Furthermore, speakers in Catalonia and Andorra, but not in Aragon, showed a clear standardization pattern, with younger speakers having dialectal pronunciations closer to the standard than older speakers. This clearly indicates the presence of a border effect within a single country with respect to word pronunciation distances. Since a great deal of scholarship focuses on single segment changes, we compare our analysis to the analysis of three segment changes that have been discussed in the literature on Catalan. This comparison revealed that the pattern observed at the word pronunciation level was supported by two of the three cases examined. As not all individual cases conform to the general pattern, t

Frisian and Dutch both have a direct speech reporting construction and an indirect speech reporting construction with verb final word order. Frisian also has an additional indirect speech reporting construction, the embedded verb-second construction which resembles direct speech in many respects. We investigated whether Frisian-Dutch bilinguals show negative transfer in their interpretation of direct speech in Dutch. We hypothesized that Frisian-Dutch bilinguals would rate an infelicitous embedded V2 construction in Dutch as higher than Dutch monolinguals. Further we hypothesized that when tested on their interpretation of direct speech reports in Dutch, Frisian-Dutch bilinguals would make more errors than their monolingual Dutch counterparts. Our results support both hypotheses.

Frisian and Dutch both have a direct speech reporting construction and an indirect speech reporting construction with verb final word order. Frisian also has an additional indirect speech reporting construction, the embedded verb-second construction which resembles direct speech in many respects. We investigated whether Frisian-Dutch bilinguals show negative transfer in their interpretation of direct speech in Dutch. We hypothesized that Frisian-Dutch bilinguals would rate an infelicitous embedded V2 construction in Dutch as higher than Dutch monolinguals. Further we hypothesized that when tested on their interpretation of direct speech reports in Dutch, Frisian-Dutch bilinguals would make more errors than their monolingual Dutch counterparts. Our results support both hypotheses.

Dialectvariatie beter in beeld te brengen Het centrale thema van Martijn Wielings proefschrift is het onderzoeken van dialectvariatie. Om een objectief beeld te krijgen van dialectvariatie meten we dialectafstanden op basis van honderden woorden automatisch op basis van genoteerde uitspraken. Wieling verfijnde deze afstandsmaat door het gebruik van (automatisch bepaalde) akoestisch gevoelige klankafstanden. Daarnaast introduceert hij een nieuwe methode die het mogelijk maakt om groepen van vergelijkbare dialecten te vinden, waarbij tegelijkertijd de onderliggende taalkundige basis (gebaseerd op klankcorrespondenties) wordt bepaald. Voor zowel Engelse als Nederlandse dialecten vond Wieling aannemelijke geografische dialectgebieden samen met hun meest karakteristieke klankcorrespondenties. In het Fries is bijvoorbeeld de toevoeging van ‘sj’ erg kenmerkend: ‘wachten’ wordt ‘wachtsje’. Wieling ontwikkelde een integrale aanpak, die de invloed van diverse factoren op dialectvariatie per woord kan bepalen. Niet alleen kan via deze methode gekeken worden naar de invloed van geografische ligging, maar ook naar de rol van verschillende sociale en woord-gerelateerde factoren (zoals leeftijd van de spreker en woordfrequentie). Wieling ontdekte bijvoorbeeld bij Nederlandse dialectwoorden dat deze het meest verschillen van de standaardtaal in de provincies Friesland, Groningen, Drenthe, Overijssel, Limburg en Zeeland. Daarnaast blijkt dat de dialectuitspraken van een gemeenschap met een klein aantal inwoners of een hoge gemiddelde leeftijd meer afwijken van de Nederlandse standaardtaal dan die van een gemeenschap met een groot aantal inwoners of een lage gemiddelde leeftijd. Ook blijkt dat meer frequente woorden meer resistent zijn tegen standaardisatie (ook in andere talen). Wielings dissertatie gaat specifiek over uitspraken in dialect. Het zegt niets over het accent van deze sprekers in de standaardtaal., The central theme of Martijn Wieling’s dissertation is dialect variation. To obtain a true picture of dialect variation, we measure dialect distances automatically on the basis of the transcriptions of hundreds of words. In this dissertation, Wieling refined this distance measure by employing (automatically generated) acoustically sensitive sound distances. Additionally, he introduced a novel method enabling the simultaneous identification of dialect areas together with their characteristic linguistic features (in terms of sound correspondences). For both English and Dutch, he found sensible dialect areas as well as their characteristic features. For example, for the Frisian area, we identify the addition of ‘sj’ as highly characteristic: wachten ‘to wait’ becomes wachtsje. inally, he has developed an integrated approach which is able to determine the influence of various factors on dialect variation per word. Not only is this method able to include the effect of geographical proximity, but it also allows a focus on various social and word-related factors (such as speaker age, or the frequency of a word). For example, Dutch dialect words differ most from their standard Dutch counterpart in the provinces of Friesland, Groningen, Drenthe, Overijssel, Limburg and Zeeland. In addition, he finds that the dialect pronunciations of a community with a small number of inhabitants or a high average age differ more from the standard Dutch language than those of a community having a large number of inhabitants or a low average age. Finally, he finds that more frequent words are more resistant to standardization (in different languages). This dissertation focuses on real dialect pronunciations. It does not say anything about the accent of people speaking the standard language.

In this study, we investigate crosslinguistic patterns in the alternation between UM, a hesitation marker consisting of a neutral vowel followed by a final labial nasal, and UH, a hesitation marker consisting of a neutral vowel in an open syllable. Based on a quantitative analysis of a range of spoken and written corpora, we identify clear and consistent patterns of change in the use of these forms in various Germanic languages (English, Dutch, German, Norwegian, Danish, Faroese) and dialects (American English, British English), with the use of UM increasing over time relative to the use of UH. We also find that this pattern of change is generally led by women and more educated speakers. Finally, we propose a series of possible explanations for this surprising change in hesitation marker usage that is currently taking place across Germanic languages.

In this study, we investigate crosslinguistic patterns in the alternation between UM, a hesitation marker consisting of a neutral vowel followed by a final labial nasal, and UH, a hesitation marker consisting of a neutral vowel in an open syllable. Based on a quantitative analysis of a range of spoken and written corpora, we identify clear and consistent patterns of change in the use of these forms in various Germanic languages (English, Dutch, German, Norwegian, Danish, Faroese) and dialects (American English, British English), with the use of UM increasing over time relative to the use of UH. We also find that this pattern of change is generally led by women and more educated speakers. Finally, we propose a series of possible explanations for this surprising change in hesitation marker usage that is currently taking place across Germanic languages.

In this study, we investigate crosslinguistic patterns in the alternation between UM, a hesitation marker consisting of a neutral vowel followed by a final labial nasal, and UH, a hesitation marker consisting of a neutral vowel in an open syllable. Based on a quantitative analysis of a range of spoken and written corpora, we identify clear and consistent patterns of change in the use of these forms in various Germanic languages (English, Dutch, German, Norwegian, Danish, Faroese) and dialects (American English, British English), with the use of UM increasing over time relative to the use of UH. We also find that this pattern of change is generally led by women and more educated speakers. Finally, we propose a series of possible explanations for this surprising change in hesitation marker usage that is currently taking place across Germanic languages.

In this study, we investigate crosslinguistic patterns in the alternation between UM, a hesitation marker consisting of a neutral vowel followed by a final labial nasal, and UH, a hesitation marker consisting of a neutral vowel in an open syllable. Based on a quantitative analysis of a range of spoken and written corpora, we identify clear and consistent patterns of change in the use of these forms in various Germanic languages (English, Dutch, German, Norwegian, Danish, Faroese) and dialects (American English, British English), with the use of UM increasing over time relative to the use of UH. We also find that this pattern of change is generally led by women and more educated speakers. Finally, we propose a series of possible explanations for this surprising change in hesitation marker usage that is currently taking place across Germanic languages.

Dialectvariatie beter in beeld te brengen Het centrale thema van Martijn Wielings proefschrift is het onderzoeken van dialectvariatie. Om een objectief beeld te krijgen van dialectvariatie meten we dialectafstanden op basis van honderden woorden automatisch op basis van genoteerde uitspraken. Wieling verfijnde deze afstandsmaat door het gebruik van (automatisch bepaalde) akoestisch gevoelige klankafstanden. Daarnaast introduceert hij een nieuwe methode die het mogelijk maakt om groepen van vergelijkbare dialecten te vinden, waarbij tegelijkertijd de onderliggende taalkundige basis (gebaseerd op klankcorrespondenties) wordt bepaald. Voor zowel Engelse als Nederlandse dialecten vond Wieling aannemelijke geografische dialectgebieden samen met hun meest karakteristieke klankcorrespondenties. In het Fries is bijvoorbeeld de toevoeging van ‘sj’ erg kenmerkend: ‘wachten’ wordt ‘wachtsje’. Wieling ontwikkelde een integrale aanpak, die de invloed van diverse factoren op dialectvariatie per woord kan bepalen. Niet alleen kan via deze methode gekeken worden naar de invloed van geografische ligging, maar ook naar de rol van verschillende sociale en woord-gerelateerde factoren (zoals leeftijd van de spreker en woordfrequentie). Wieling ontdekte bijvoorbeeld bij Nederlandse dialectwoorden dat deze het meest verschillen van de standaardtaal in de provincies Friesland, Groningen, Drenthe, Overijssel, Limburg en Zeeland. Daarnaast blijkt dat de dialectuitspraken van een gemeenschap met een klein aantal inwoners of een hoge gemiddelde leeftijd meer afwijken van de Nederlandse standaardtaal dan die van een gemeenschap met een groot aantal inwoners of een lage gemiddelde leeftijd. Ook blijkt dat meer frequente woorden meer resistent zijn tegen standaardisatie (ook in andere talen). Wielings dissertatie gaat specifiek over uitspraken in dialect. Het zegt niets over het accent van deze sprekers in de standaardtaal., The central theme of Martijn Wieling’s dissertation is dialect variation. To obtain a true picture of dialect variation, we measure dialect distances automatically on the basis of the transcriptions of hundreds of words. In this dissertation, Wieling refined this distance measure by employing (automatically generated) acoustically sensitive sound distances. Additionally, he introduced a novel method enabling the simultaneous identification of dialect areas together with their characteristic linguistic features (in terms of sound correspondences). For both English and Dutch, he found sensible dialect areas as well as their characteristic features. For example, for the Frisian area, we identify the addition of ‘sj’ as highly characteristic: wachten ‘to wait’ becomes wachtsje. inally, he has developed an integrated approach which is able to determine the influence of various factors on dialect variation per word. Not only is this method able to include the effect of geographical proximity, but it also allows a focus on various social and word-related factors (such as speaker age, or the frequency of a word). For example, Dutch dialect words differ most from their standard Dutch counterpart in the provinces of Friesland, Groningen, Drenthe, Overijssel, Limburg and Zeeland. In addition, he finds that the dialect pronunciations of a community with a small number of inhabitants or a high average age differ more from the standard Dutch language than those of a community having a large number of inhabitants or a low average age. Finally, he finds that more frequent words are more resistant to standardization (in different languages). This dissertation focuses on real dialect pronunciations. It does not say anything about the accent of people speaking the standard language.