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By-participant mean proportion of voiced stops prevoiced in German and Spanish, separated by cognates vs. noncognates and pretest (before immersion) vs. posttest (after immersion). Error bars show bootstrapped 95% confidence intervals.

By-participant mean proportion of voiced stops prevoiced in German and Spanish, separated by cognates vs. noncognates and pretest (before immersion) vs. posttest (after immersion). Error bars show bootstrapped 95% confidence intervals.

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Article
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Speakers learning a second language show systematic differences from native speakers in the retrieval, planning, and articulation of speech. A key challenge in examining the interrelationship between these differences at various stages of production is the need for manual annotation of fine-grained properties of speech. We introduce a new method fo...

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... 3, t = 4.62, p <.001). In contrast to the RT results, the main effect of cognate was Figure 7 shows the results for voiced stops. Speakers were not able to reliably distinguish German and Spanish phonetic properties; prevoicing rates were not significantly different in Spanish vs. German (main effect of target language (β = -0.9, ...

Citations

... Praat 55 69 84 130 230 267 70 94 154 64 105 125 54 81 112 57 75 95 semivowels 68 80 103 136 295 334 89 126 222 83 122 154 67 114 168 67 90 130 nasal 75 112 106 219 409 381 96 229 239 67 120 112 66 175 151 74 130 125 fricatives 91 113 125 564 593 700 209 263 439 As seen in the results, replicating the performance obtained from the trained samples to a new domain is a critical obstacle for machine-learning-based methods [20,21]. There is a performance gap for DeepFormants and our model when we combine samples from the train set of Clopper and Hillenbrand during the optimization compared to train only with the VTR. ...
... Praat 55 69 84 130 230 267 70 94 154 64 105 125 54 81 112 57 75 95 semivowels 68 80 103 136 295 334 89 126 222 83 122 154 67 114 168 67 90 130 nasal 75 112 106 219 409 381 96 229 239 67 120 112 66 175 151 74 130 As seen in the results, replicating the performance obtained from the trained samples to a new domain is a critical obstacle for machine-learning-based methods [20,21]. There is a performance gap for DeepFormants and our model when we combine samples from the train set of Clopper and Hillenbrand during the optimization compared to train only with the VTR. ...
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Formants are the spectral maxima that result from acoustic resonances of the human vocal tract, and their accurate estimation is among the most fundamental speech processing problems. Recent work has been shown that those frequencies can accurately be estimated using deep learning techniques. However, when presented with a speech from a different domain than that in which they have been trained on, these methods exhibit a decline in performance, limiting their usage as generic tools. The contribution of this paper is to propose a new network architecture that performs well on a variety of different speaker and speech domains. Our proposed model is composed of a shared encoder that gets as input a spectrogram and outputs a domain-invariant representation. Then, multiple decoders further process this representation, each responsible for predicting a different formant while considering the lower formant predictions. An advantage of our model is that it is based on heatmaps that generate a probability distribution over formant predictions. Results suggest that our proposed model better represents the signal over various domains and leads to better formant frequency tracking and estimation.
Article
Albert Costa was a dear friend and colleague who died young but accomplished much. We provide a brief sketch of his scientific contributions to the field of psycholinguistics and bilingualism. The articles included in the special issue are then presented along three research topics developed by Albert Costa in his own career: Lexical access in bilingualism, executive control in bilingualism, and judgement and decision making in a foreign language. The articles explore topics such as competition within and across words in unimodal or bimodal bilinguals, and its links to domain-general executive control, the reshaping of word form knowledge following second language learning, the stakes and methods involved in investigating accented speech, and the contrast between decision making in the native or second language. We hope this collection provides an up-to-date perspective on the rich field of bilingualism research, and a modest homage to our late friend and colleague.