@article {491,
	title = {Analyzing time-dynamic vowels: normalization in the current decade},
	year = {2021},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {online},
	abstract = {With the exception of van der Harst (2011), previous work comparing vowel-normalization methods for sociophonetic research has largely focused on steady-state vowels (e.g. Adank et al 2004). With modern sociophonetics nowadays resorting more to time-dynamic analyses rather than steady-state approaches, the present study revisits the issue of vowel normalization with an explicit consideration of temporal trajectories. We compared sixteen normalization methods implemented in Visible Vowels (Heeringa \& Van de Velde 2018) and an unnormalized baseline, using van der Harst (2011){\textquoteright}s hand-corrected data of the Flemish-Dutch Teacher Corpus, which includes fifteen vowels (of which six may be phonologically diphthongal, but in practice all of these are temporally variable to some extent; van der Harst 2011). Using generalized additive models to model temporal trajectories, we compared the normalization methods{\textquoteright} abilities to normalize anatomical variation, retain vowel distinctions and explain variation in the normalized F0-F3; similar criteria were also used by Adank et al (2004) and van der Harst (2011). We additionally investigated the extent to which by-speaker random effects could supplement or perhaps even replace the use of normalization. Our results partly reproduce the good results for Lobanov, Gerstman, and Nearey I found earlier. However, we observe that other methods, particularly Heeringa \& Van de Velde II, come close to their performance but at much higher effect sizes. We also observe that random effects are not only useful for the unnormalized baseline, but provide a similar benefit also for the normalized data, showing that they are complementary to normalization. We interpret our findings in light of the way the different methods handle temporal dynamics.

References

Adank, P., Smits, R., \& Van Hout, R. (2004). A comparison of vowel normalization procedures for language variation research. The Journal of the Acoustical Society of America, 116(5), 3099-3107.
van der Harst, S. (2011). The vowel space paradox. A Sociophonetic Study on Dutch. LOT.
Heeringa W., \& Van de Velde, H. (2018). Visible Vowels: a Tool for the Visualization of Vowel Variation. In: Inguna Skadina \& Maria Eskevich (Eds.). Proceedings CLARIN Annual Conference 2018, 8-10 October, Pisa, Italy (pp. 120-123). CLARIN ERIC.
},
	author = {Voeten, Cesko and Wilbert Heeringa and Hans Van de Velde}
}
@article {492,
	title = {Effects of obstruent voicing on vowel fundamental frequency in Dutch},
	year = {2021},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {online},
	abstract = {It has been known for a long time and for a wide variety of languages that vowel fundamental frequency (F0) can be affected by the intrinsic properties of the preceding consonants. In particular, F0 following voiceless obstruents tends to be significantly higher than F0 following voiced obstruents (e.g., House and Fairbanks, 1953; Hombert et al., 1979). There has been a long-standing debate about the cause of this phenomenon. Some evidence in previous work is more compatible with a physiological/aerodynamic account of this effect (e.g., Halle and Stevens, 1967; Kirby \& Ladd, 2016), while other supports an auditory enhancement account (e.g., Kingston and Diehl 1994).

This paper investigates these consonant-related F0 perturbations in Dutch after initial fricatives (/v f/) and stops (/b p/), as compared to after sonorant /m/, in recordings by Pinget (2015). Dutch is particularly interesting for the investigation of F0 perturbations for two reasons: 1) Dutch {\textendash} unlike English {\textendash} is a true voicing language and 2) Dutch initial fricatives are currently undergoing a process of devoicing (e.g., Kissine et al., 2003; Pinget, 2015). 

The F0 contours in isolated words were analyzed using GAMM (e.g., Wieling 2018) with /m/ as baseline condition. Results show that after unvoiced /p, f/, the F0 at vowel onset is significantly higher than this baseline. Moreover, voicing measures (degree of voicing, duration, and VOT) interact with the main effects of onset consonant on F0. Especially after /v/, F0 at vowel onset increased as the voicing measures of preceding /v/ decreased. Thus, we found no trace of an active gesture to explicitly lower F0 after highly devoiced fricatives, as would be predicted by an auditory enhancement account. In conclusion, these results regarding F0 contours, the time course of the effects and the covariation patterns are taken as evidence to support a physiological/aerodynamic cause of F0 perturbations.

References

Halle, M., \& Stevens, K. (1967). Mechanism of glottal vibration for vowels and consonants. The Journal of the Acoustical Society of America, 41(6), 1613-1613.
Hombert, J.-M., Ohala, J. J., \& Ewan, W. G. (1979). Phonetic explanations for the development of tones. Language 55, 37{\textendash}58. 
House, A. S., \& Fairbanks, G. (1953). The influence of consonant environment upon the secondary acoustical characteristics of vowels. Journal of the Acoustical Society of America, 25, 105{\textendash}113.
Kingston, J., \& Diehl, R. L. (1994). Phonetic knowledge. Language, 70, 419{\textendash}454.
Kirby, J. P., \& Ladd, D. R. (2016). Effects of obstruent voicing on vowel F 0: Evidence from {\textquotedblleft}true voicing{\textquotedblright} languages. The Journal of the Acoustical Society of America, 140(4), 2400-2411.
Kissine, M., Van de Velde, H., \& van Hout, R. (2003). An acoustic study of standard Dutch /v/, /f/, /z/ and /s/. Linguistics in the Netherlands, 20(1), 93{\textendash}104.
Pinget, A. (2015). The actuation of sound change. PhD Dissertation, Utrecht University, The Netherlands.
Wieling, M. (2018). Analyzing dynamic phonetic data using generalized additive mixed modeling: A tutorial focusing on articulatory differences between L1 and L2 speakers of English. Journal of Phonetics, 70, 86-116.
},
	author = {Anne-France Pinget and Hugo Quen{\'e}}
}
@article {496,
	title = {Effects of simultaneous bilingualism and speech style on allophonic variation:  A study of the Finland-Swedish front rounded vowels [{\o}:] and [{\oe}:]},
	year = {2021},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {online},
	abstract = {This study examines sociophonetic variation and change in Finland-Swedish, a variety of Swedish spoken by 290,000 native speakers in Finland. In the last century, urbanisation of historically Swedish speaking regions in Finland has resulted in increasing dominance of Finnish in society and, consequently, growing bilingualism within the Finland-Swedish community. The study, which is one of the first to investigate variation in Finland-Swedish using acoustic analysis, explores crosslinguistic transfer in the phonetic variants of /{\o}:/ in speech produced by simultaneous bilingual Finland-Swedish individuals. In Swedish, the phoneme /{\o}:/ can be realised as the allophones [{\o}:] and [{\oe}:], while in Finnish, only [{\o}:] is used. We investigate whether influence from Finnish may lead to decreased distinction between the allophones for simultaneous Finnish and Finland- Swedish bilinguals, examining transfer through the Speech Learning Model framework (Flege, 1995; 2007) by observing possible interactions between bilinguals{\textquoteright} two languages in their phonetic space. Acoustic analysis is used to compare height and fronting of allophones [{\o}:] and [{\oe}:] produced by 115 participants in three speech styles on a continuum of formality (Labov, 2006). The statistical analysis indicates increased overlap of [{\o}:] and [{\oe}:] in the vowel spaces of bilingual speakers, who produced [{\o}:] with higher F1 and lower F2 values and [{\oe}:] with lower F1 and higher F2 values compared to monolingual participants, particularly in spontaneous, informal speech. The results suggest a potential effect of Finnish phonetic transfer in the production of Finland-Swedish allophones by simultaneous bilinguals in the form of partial category assimilation of Finnish and Finland-Swedish /{\o}:/. Additionally, the findings highlight the importance of considering intra-speaker variation in bilingual transfer research.

References

Flege, J.E. (1995). Second language speech learning: Theory, findings, and problems. Speech perception and linguistic experience: Issues in cross-language research, 92, 233{\textendash}277.
Flege, J.E. (2007). Language contact in bilingualism: Phonetic system interactions. In J. Cole and J. Hualde (Eds.), Laboratory phonology 9 (pp. 353{\textendash}382). Mouton de Gruyter.	
Labov, W. (2006). The social stratification of English in New York City. Cambridge University Press.
},
	author = {Strandberg, Janine A.E. and Charlotte Gooskens and Anja Sch{\"u}ppert}
}
@article {493,
	title = {Focus and accent in English},
	year = {2021},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {online},
	abstract = {Contrastive focus in English is marked with a rising accent (autosegmentally L+H*) and broad (all new) focus with a high accent (H*). However, inconclusive production and perception evidence supports the idea that L+H* is simply an emphatic version of H*, not phonologically distinct from it.  We used Rapid Prosody Transcription to test these two views. Forty-seven speakers of Standard Southern British English (SSBE) listened to 86 SSBE utterances and marked the words they considered prominent or emphatic. Accents (N = 281) were independently coded as H* or L+H* using phonetic criteria, and as contrastive or non-contrastive using pragmatic criteria. If L+H* is an emphatic H*, L+H*s should be rated more prominent than H*s; if the accents encode a pragmatic distinction, contrastive accents should be rated more prominent than non-contrastive ones. The results showed effects of both accent and pragmatics (L+H* > H*; contrastive > non-contrastive) and no interaction. Contrastive L+H*s were rated most prominent, non-contrastive H*s least prominent, while non-contrastive L+H*s and contrastive H*s had average and almost identical ratings. Participants used different strategies: some focused on accent type, others on pragmatics, and still others made neither distinction. These results suggest that a reason for the continuing debate about H* and L+H* may be that the accents form a weak contrast which some speakers acquire and attend to while others do not. Similarly, researchers who focus on contrastive L+H* and non-contrastive H* see distinct categories, while those who focus on non-contrastive L+H*s and contrastive H*s tend to see a continuum.},
	author = {Arvaniti, Amalia and Gryllia, Stella and Zhang, Cong and Katherine Marcoux}
}
@article {487,
	title = {Jouw {\textquotedblleft}voornaam{\textquotedblright} is niet mijn {\textquotedblleft}voornaam{\textquotedblright}:  An acoustic analysis of individual talker differences in producing lexical stress in Dutch},
	year = {2021},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {online},
	abstract = {Different people talk differently, even speakers from the same region. This individual variability results in large acoustic variability in speech, both at the segmental level (productions of vowels and consonants) and the suprasegmental, or prosodic, level (e.g., lexical stress). While individual differences in segment production are well established in the literature, relatively little is known about how individual talkers differ in their prosody. The present study examined individual-talker differences in productions of lexical stress. We recorded 744 tokens of Dutch segmentally overlapping words (e.g., VOORnaam vs. voorNAAM; {\textquoteleft}first name{\textquoteright} vs. {\textquoteleft}respectable{\textquoteright}) in variable sentence contexts from 40 native speakers of Dutch (balanced gender; relatively homogeneous Nijmegen-centered sample), and measured acoustic cues to lexical stress (mean F0, F0 variation, duration, spectral tilt, intensity, and vowel quality). Linear Discriminant Analyses (LDA) on data from each individual participant yielded sets of cue-weights for each participant, informing us on their phonetic cue-weighting strategies. Results showed {\textendash} on top of a general trend to primarily use mean F0, intensity, and duration {\textendash} that each participant also employed a unique combination of cues to signal lexical stress, illustrating large prosodic variability between talkers. Moreover, classes of cue-weighting strategies emerged, with a large group of primarily F0-weighting talkers and another group of primarily intensity-weighting talkers. Furthermore, based on LDA accuracy scores, we confirmed that spectral tilt was a more reliable cue to lexical stress than intensity for /a:/. However, when 9 other vowels were included in the analysis, this advantage disappeared, suggesting that for a larger sample of Dutch vowels both cues are equally important. Together, these outcomes contribute to a more comprehensive acoustic description of lexical stress in Dutch, allowing group-level and individual-talker inferences.},
	author = {Severijnen, Giulio G.A. and Hans Rutger Bosker and James M. McQueen}
}
@article {494,
	title = {Linguistic asymmetries in cross-cultural emotion recognition},
	year = {2021},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {online},
	abstract = {The aim of this study is to explore cross-linguistic/cross-cultural recognition of emotion in a relatively understudied language (Korean). Previous studies on cross-linguistic emotion recognition found that intercultural emotion recognition is the combination of universal, cultural and, to a lesser extent, linguistic factors (Mesquita \& Fridja, 1992; Scherer, 1997a, 1997b). However, these studies have mostly adopted unbalanced experimental designs that either used a {\textquotedblleft}one-to-many{\textquotedblright} approach (presenting the same stimuli to different listener groups), or a {\textquotedblleft}many-to-one{\textquotedblright} approach (presenting different stimuli to the same listeners). Moreover, these studies are often limited to basic emotions (Ekman, 1992). Consequently, current work on cross-linguistic emotion recognition may fail to take linguistic asymmetries into account. Thus, in the present study, we developed a full cross-language design ({\textquotedblleft}two-to-two{\textquotedblright}), with speakers and listeners from two typologically different languages and different cultures, Dutch and Korean. Participants heard carrier phrases that are linguistically appropriate in both languages ([nuto hɔm sɛpika{\ng}]) expressed with eight different emotions (balanced in terms of valence and arousal), and were then asked to choose one of the eight emotions that they thought the speaker was expressing. 

Consistent with previous studies, our data revealed that both groups can successfully decode the emotions above chance level, supporting the universal hypothesis (Scherer et al., 2001). Also consistent with previous work (Elfenbein \& Ambady, 2002), our data highlighted an in-group advantage for recognizing vocal emotions produced in listeners{\textquoteright} native language. The similarities and differences in confusion patterns across languages will be discussed in terms of the influence of linguistic asymmetries. 

References

Ekman, P. (1992). An argument for basic emotions. Cognition and Emotion, 6, 169{\textendash} 200. 
Elfenbein, H. A., \& Ambady, H. (2002). On the universality and cultural specificity of emotion recognition: a meta-analysis. Psychological bulletin, 128, 203-235. 
Mesquita, B., \& Frijda, N. H. (1992). Cultural variations in emotions: A review. Psychological Bulletin, 112, 179 {\textendash}204. 
Scherer, K. R. (1997a). Profiles of emotion-antecedent appraisal: Testing theoretical predictions across cultures. Cognition and Emotion, 11, 113-150. 
Scherer, K. R. (1997b). The role of culture in emotion-antecedent appraisal. Journal of Personality and Social Psychology, 73, 902-922. 
Scherer, K. R., Banse, R., \& Wallbott, H. G. (2001). Emotion inferences from vocal expression correlate across languages and cultures. Journal of Cross Culture Psychology, 32, 76-92.
},
	author = {Liang, Yachan and Mirjam Broersma and Martijn Goudbeek and Konopka, Agnieszka and Choi, Jiyoun}
}
@article {489,
	title = {One speaker, two languages: Within-speaker variation of [s] across L1 Dutch and L2 English},
	year = {2021},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {online},
	abstract = {The voiceless alveolar fricative [s] is phonetically similar but not identical in the Dutch and English language. This makes it one of the most difficult English segments to pronounce correctly for native (L1) speakers of Dutch (Flege, 1995). If second language (L2) speakers fail to realize that the Dutch and English [s] have phonetic differences, they may place them into one phonetic category and use their Dutch [s] also when speaking English. Such L1 transfer would be useful for forensic speech science, as it would allow for the inclusion of these segments in cross-linguistic comparisons. 

According to Quen{\'e} et al. (2017), who looked into read speech [s] by L1 Dutch speakers with a relatively high proficiency of L2 English, different [s] realizations are found in L1 Dutch than in L2 English. This implies that [s] is not useful as a feature to perform cross-linguistic speaker comparisons. However, these same speakers have also been recorded producing spontaneous speech, which may be considered more representative for forensic casework data and may evoke less formal language use. Hence, this study investigates the language-dependency of [s] in spontaneous speech.

The language-dependency of [s] was evaluated by comparing the Centre of Gravity (CoG), its standard deviation (SD), and spectral tilt of [s] productions in the same speakers{\textquoteright} L1 Dutch and L2 English (N = 45). Analyses are underway and will be presented at the conference.

References

Flege, J. E. (1995). Second language speech learning: Theory, findings, and problems. Speech perception and linguistic experience: Issues in cross-language research, 92, 233-277.
Quen{\'e}, H., Orr, R., \& van Leeuwen, D. (2017). Phonetic similarity of/s/in native and second language: Individual differences in learning curves. Journal of the Acoustical Society of America, 142(6), EL519-EL524.
},
	author = {Meike de Boer and Willemijn Heeren and Pierce-Melly, Sophie}
}
@article {485,
	title = {Over de herkenning van (licht) gereduceerde woorden {\textendash} het suffix -en in het Vlaams en het Nederlands},
	year = {2021},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {online},
	abstract = {Tijdens natuurlijke conversaties worden woorden vaak korter uitgesproken dan hun canonieke vorm doet vermoeden. Zo wordt bijvoorbeeld het Nederlandse suffix -en [-ən], wat kenmerkend is voor de infinitief, vaak als [-ə] uitgesproken [1]. Gewoonlijk herkennen luisteraars frequente woorden sneller dan minder frequente woorden, maar recent onderzoek toont aan dat hoewel gereduceerde woorden het meest frequent zijn, luisteraars de canonieke vorm minstens even snel herkennen [2].

Het Nederlands biedt een interessant testgeval om de invloed van frequentie en canoniciteit in woordherkenningsproces te evalueren. In de spontane spraak is de gereduceerde vorm [-ə] van -en frequenter in Nederland dan in Vlaanderen [3]. De voorspelling is dan dat zowel Nederlanders als ook Vlamingen goed in het herkennen van de canonieke vorm zijn maar Vlamingen wat sneller de canonieke vorm zullen herkennen omdat voor hen canoniciteit en frequentie aligneren.

Dit werd getest door een auditieve lexicale decisietaak met 45 deelnemers uit Nederland en 40 deelnemers uit Vlaanderen. Aan hen werd gevraagd om naar 348 bestaande en nietbestaande woorden in de korte frase {\textquotedblleft}X bestaat{\textquotedblright} te luisteren waaronder 188 woorden met -en. Daarbij werd de helft van de woorden met het suffix -en ingekort gepresenteerd. 

Het resultaat toont aan dat beide groepen sneller op de canonieke vorm dan op de gereduceerde vorm reageerden. Het voordeel van de canonieke vorm was even groot zowel voor Nederlandse alsook Vlaamse luisteraars ook al komt de gereduceerde vorm vaker in de spreektaal in Nederland voor. Dit laat zien dat het voordeel van de canonieke vorm voor het Nederlands geldt, onafhankelijk van de fonologische variatie van spreektaal.

Referenties

[1] Van de Velde, H. \& van Hout, R. (2000). N-deletion in reading style. Linguistics in the Netherlands, 17(1), 209-219.
[2] Sumner, M., Kim, S. K., King, E., \& McGowan, K. B. (2014). The socially weighted encoding of spoken words: a dual-route approach to speech perception. Frontiers in Psychology, 4, 1015. 
[3] Van de Velde, H. \& van Hout, R. (2003). De deletie van de slot-n. Nederlandse Taalkunde 8(2), 93-114.
},
	author = {Weihs, Nina and Eva Reinisch and Holger Mitterer}
}
@article {488,
	title = {Phonetic alignment of /x/ in interaction},
	year = {2021},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {online},
	abstract = {Alignment is the process of adapting speech to another interlocutor{\textquoteright}s speech. We investigated phonetic alignment to two variants of the Dutch /x/, also known as the {\textquotedblleft}hard g{\textquotedblright} or {\textquotedblleft}soft g{\textquotedblright}. We set out to explore whether phonetic alignment is caused only by short-term priming, long-term priming or by speakers remembering features of the speech of their interlocutor. Phonetic alignment effects are usually found in rather controlled environments (e.g., shadowing tasks; Pardo et al., 2013) or using an AXB assessment (Pardo, 2006). We studied this phenomenon in less constricted environments and using automatised measurements. 

Participants interacted with two different confederates, one producing a {\textquotedblleft}hard g{\textquotedblright} and one a {\textquotedblleft}soft g{\textquotedblright} in a sentence completion task. Participants completed a total of 268 sentences. In a pre-test, participants first completed sentences by themselves. Then, they interacted with Confederate 1 in Round 1, with Confederate 2 in Round 2, and again with Confederate 1 in a so-called inter-test and in Round 3, and lastly by themselves again in the post-test. 

We investigated the duration (van der Harst, van de Velde \& Schouten, 2007) and Centre of Gravity of the 15085 fricatives of 36 participants. We investigated three different predictors: the last produced /x/ of the confederate, the average of the ten last produced /x/s of the confederate and the average of all heard /x/s. None of the predictors showed significant effects. Descriptive analyses showed tremendous variation among speakers, likely unrelated to alignment. We conclude that alignment at the sound level is not as clear as previously demonstrated in less ecologically valid studies. 

References 

Pardo, J. S. (2006). On phonetic convergence during conversational interaction. The Journal of the Acoustical Society of America, 119(4), 2382-2393. 
Pardo, J. S., Jordan, K., Mallari, R., Scanlon, C., \& Lewandowski, E. (2013). Phonetic convergence in shadowed speech: The relation between acoustic and perceptual measures. Journal of Memory and Language, 69(3), 183-195. 
Van der Harst, S., Van de Velde, H., \& Schouten, B. (2007). Acoustic characteristics of Standard Dutch. age, 22(40), 45-60.
},
	author = {Lotte Eijk and Schriefers, Herbert and Mirjam Ernestus}
}
@article {495,
	title = {Phonetic contrast between error and correct target is a major factor in determining whether segmental errors are detected "early", "late", or not at all},
	year = {2021},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {online},
	abstract = {In self-monitoring for speech errors, some segmental speech errors are detected "early", i.e. before articulation is initiated, and others "late", i.e. after articulation has started, and others again are detected not at all. What causes these differences in detecting speech errors? We hypothesize that detection of errors that are similar to the correct target takes longer than detection of errors that are dissimilar. We also hypothesize that the time available for error detection in internal speech is limited. If this time is exceeded before an error is detected, self-monitoring gets a second chance during or after articulation. If also the time available for this second stage of error detection is exceeded, then the error remains undetected. From these hypotheses we predict that relatively more "dissimilar" than "similar" segmental errors are detected before articulation, and also that relatively more "similar" than "dissimilar" errors remain undetected.  To test these hypotheses, we have assessed, in data obtained in two earlier published SLIP experiments, the relative frequencies of repaired and unrepaired segmental speech errors that differ systematically in strength of phonetic contrast with the correct target. Results show that indeed a major factor affecting the distribution of errors over "early", "late" and "undetected" is the strength of phonetic contrast between two competing response candidates.},
	author = {Sieb Nooteboom and Hugo Quen{\'e}}
}
@article {490,
	title = {Prosodic expression of sarcasm in L2 English after explicit training},
	year = {2021},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {online},
	abstract = {Sarcasm occurs frequently in everyday interactions and is typically expressed via prosody. However, speakers usually experience difficulty with expressing sarcasm prosodically in a second language. Recent research by Smorenburg and co-authors has shown that short prosodic training can already improve perceived sarcasm in Dutch learners of English (L2E learners). It remains to be investigated what exactly have changed in the prosodic production after training. 

In this study, we analysed the production of sarcastic prosody by L2E learners (N =12) before and after the training in Smorenburg et al.{\textquoteright}s study. The production data were elicited via a simulated telephone conversation task in which the participants were prompted to give a sarcastic response to a friend{\textquoteright}s remark. Mean pitch, maximum, minimum pitch, duration were extracted from the key word of each response (e.g. {\textquoteleft}healthy{\textquoteright} in {\textquoteleft}She is a healthy lady) using ProsodyPro and were subsequently analysed using linear mixed-effect modelling. 

We have found that prosodic improvement depended on utterance type and gender. The training helped L2E learners to lengthen duration in declaratives and tag-questions but not in wh-exclamatives and lower mean pitch and minimum pitch across utterance types to sound more sarcastic, similar to native speakers of English. Male L2E learners lengthened duration to a larger extent than female L2E learners, similar to female native speakers of English; female L2 learners did not lower mean pitch more than male L2 learners, different from female native speakers of English. We will discuss the nature of the prosodic improvement: L1 transfer and/or L2 learning.
},
	author = {Zandee, Femke and Jansen, Nelleke and Laura Smorenburg and Aoju Chen}
}
@article {484,
	title = {An exploratory study into interspeaker variation in creaky voice in Dutch},
	year = {2021},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {online},
	abstract = {Creaky voice occurs regularly in speech, although we are usually unaware of it. In a language such as Dutch, creaky voice is not phonemic but nevertheless often occurs as a result of changes in subglottal air pressure throughout an utterance. It has been reported that there appears to be a large amount of interspeaker variation in creaky voice (Bőhm \& Shattuck-Hufnagel, 2009; Kuang, 2017; Dallaston \& Docherty, 2020), but this has typically been observed incidentally and there have only been a few studies dedicated to characterising this variation.

In the current study, we have examined the conversational speech of 30 men recorded in the Corpus of Spoken Dutch (Oostdijk, 2000). Data were manually annotated for occurrences of creaky voice and each creaky interval was categorised as one of several subtypes of creaky voice based on the degree of periodicity (cf. Keating et al., 2015). Each annotated interval was also analysed acoustically with measures of f0, cepstral peak prominence, and measures of spectral balance.

Our results show that interspeaker variation in creaky voice is indeed considerable. The frequency with which creaky voice occurs ranges from very low in some speakers to notably high in others. Speakers also use the subtypes of creaky voice in different proportions. Furthermore, there are interspeaker differences in the acoustic parameters, which allows for above-chance speaker classification by means of linear discriminant analysis. These results suggest that measures of voice quality may be useful in speaker discrimination applications, such as forensic voice comparisons.

References

Bőhm, T., \& Shattuck-Hufnagel, S. (2009). Do Listeners Store in Memory a Speaker{\textquoteright}s Habitual 	Utterance-Final Phonation Type? Phonetica, 66, 150{\textendash}168.
Dallaston K., \& Docherty G. (2020). The quantitative prevalence of creaky voice (vocal fry) in varieties 	of English: A systematic review of the literature. PLoS ONE, 15(3): e0229960.
Keating, P., Garellek, M., \& Kreiman, J. (2015). Acoustic properties of different kinds of creaky voice. In Proceedings of the 18th International Congress of Phonetic Sciences, August 10-14, Glasgow, Scotland, 2015.
Kuang, J. (2017). Covariation between voice quality and pitch: Revisiting the case of Mandarin creaky voice. The Journal of the Acoustical Society of America, 142, 1693{\textendash}1706.
Oostdijk, N. (2000). Het Corpus Gesproken Nederlands. Nederlandse Taalkunde, 5, 280{\textendash}284.
},
	author = {van Hugte, Thom and Willemijn Heeren}
}
@article {486,
	title = {The influence of task type and personality on EFL learners{\textquoteright} oral fluency},
	year = {2021},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {online},
	abstract = {Main Research Questions: 
1) What are the contrasts in utterance and perceived fluency of low proficiency L2 English students between a dialogic and a monologic task type? 
2) To what extent is there a contrast in fluency between speakers as a consequence of performance features including anxiety, dominance and involvement?

Fluency is a term often used in relation to second language (L2) speaking proficiency. In general terms, most people have a notion of what the concept of fluency entails, but the literature shows that there is  no consensus on what precisely the concept might be (Pinget, Bosker \& de Jong, 2014). In the last decade, a growing number of studies have aimed at dissecting fluency by studying the difference between utterance and perceived fluency (e.g. Pr{\'e}fontaine, 2010; Bosker, Pinget, Quene, Sanders, \& de Jong. 2012), developing PRAAT scripts to measure fluency (de Jong et al. 2021) and distinguishing monologic from dialogic speech fluency (Tavakoli, 2016). 

This study aims to further advance our understanding of factors affecting L2 fluency. Specifically, we focus on the effect of task type and aspects of speaker personality. 

The study largely consists of two phases. Firstly, both the monologic and dialogic speech fluency of 33 third level L2 English students who did not pass or barely passed (10/20) an English speaking proficiency examination was analysed to instrumentally measure their utterance/objective fluency. Secondly, the ratings of 41 assessors who listened to 20 short audio tracks each were analysed and compared to the objective fluency results. The group assessing the tracks consisted of three groups of listeners: native English speakers, EFL higher education students, and bilingual speakers of Dutch and English who did not study languages at university or in college.

Results showed that in terms of objective fluency there was no positive effect on utterance fluency nor on perceived/subjective fluency for dialogic speech compared to monologic speech, contradicting previous research. Furthermore, perceived fluency results only partly correlated with utterance fluency results and listener groups seemed to differ in what manner they rated L2 speakers. Results also showed correlation between aspects of personality such as Foreign Language Anxiety and involvement, and speaking fluency. 

Further research using different tasks may further contribute to our understanding of fluency and its different components. Additionally, including open questions in the questionnaire would enable us to examine listeners{\textquoteright} ratings in a qualitative manner. 

Sources

Bosker, H; Pinget, A; Quen{\'e}, H; Sanders, T; de Jong, N. (2012). What makes speech sound fluent? The contributions of pauses, speed and repairs. Language Testing, 30(2), 159 {\textendash} 175.
De Jong N. Pacilly J. Heeren W. (2021). PRAAT scripts to measure speed fluency and breakdown fluency in speech automatically. Assessment in Education: Principles, Policy \& Practice.
Pinget, A. Bosker, H. Quen{\'e}, H. de Jong, N. (2014). Native speakers{\textquoteright} perceptions of fluency and accent in L2 speech. Language Testing, 31(3), 349-365.
Pr{\'e}fontaine, Y. (2010). Differences in Perceived Fluency and Utterance Fluency across Speech Elicitation Tasks: A Pilot Study. Lancaster University Postgraduate Conference in Linguistics \& Language Teaching 2010, 134-150.
Tavakoli, P. (2016). Fluency in monologic and dialogic task performance: Challenges in defining and measuring Fluency. International Review of Applied Linguistics in Language Teaching, 54(2), 133-150.
},
	author = {Van Mossevelde, Tom and Simon, Ellen and Chan, David}
}
@article {498,
	title = {A speech recognizer for subtitling Frisian/Dutch council meetings},
	year = {2021},
	publisher = {Nederlandse Vereniging voor Fonetische Wetenschappen},
	address = {online},
	abstract = {Late 2020, the Fryske Akademy was granted a project to develop a subtitling service for council meetings of Frisian municipalities. The project was financed by the Province of Frysl{\^a}n, the {\textquotedblleft}Wetterskip{\textquotedblright} and a number of Frisian municipalities. An existing speech recognizer for Frisian named FAME!, was repurposed for a new application domain: council meetings (FAME! was trained and tested on radio broadcasts only). The council meeting domain is difficult for speech recognition because of the acoustic background noise, speaker overlap and the jargon language typically used in council meetings.

To train the new recognizer, we used the radio broadcast materials utilized for the FAME! recognizer and in addition, newly created manually transcribed audio recordings of council meetings from several Frisian municipalities. The council meeting audio recordings consist of approximately 49 hours of speech, with 26 hours of Frisian speech and 23 hours of Dutch speech. Further, we obtained texts in the domain of council meetings, namely council meeting minutes and council policy documents containing approximately 11 million words; 1.1 million Frisian words and 9.9 million Dutch words. We describe the methods used to train the new recognizer, report the observed word error rates, and perform an error analysis on remaining errors.
},
	author = {Henk van den Heuvel and Bentum, Martijn and Louis ten Bosch and Wills, Simone}
}
