Search Results - "Bartelds, Martijn"

A New Acoustic-Based Pronunciation Distance Measure by Bartelds, Martijn, Richter, Caitlin, Liberman, Mark, Wieling, Martijn

“…We present an acoustic distance measure for comparing pronunciations, and apply the measure to assess foreign accent strength in American-English by comparing…”
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Minority language happiness: The link between social inclusion, well-being, and speaking a regional language in the northern Netherlands by Brouwer, Jelle, Buurke, Raoul, van den Berg, Floor, Knooihuizen, Remco, Loerts, Hanneke, Bartelds, Martijn, Wieling, Martijn, Keijzer, Merel

“…Belonging to groups is often based on shared features between members and is associated with higher levels of (social) well-being. One especially strong marker…”
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Intergenerational Language Transmission of Frisian and Low Saxon in the Netherlands by Buurke, Raoul, Bartelds, Martijn, Heeringa, Wilbert, Knooihuizen, Remco, Wieling, Martijn

“…An important mechanism for language maintenance is transmission from parents to their children. This mechanism is stronger for the regional language Frisian…”
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Neural representations for modeling variation in speech by Bartelds, Martijn, de Vries, Wietse, Sanal, Faraz, Richter, Caitlin, Liberman, Mark, Wieling, Martijn

“…•Neural acoustic models can be used to automatically model pronunciation variation.•Pronunciation variation is best captured by intermediate layers of…”
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Quantifying Language Variation Acoustically with Few Resources by Bartelds, Martijn, Wieling, Martijn

“…Deep acoustic models represent linguistic information based on massive amounts of data. Unfortunately, for regional languages and dialects such resources are…”
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Making More of Little Data: Improving Low-Resource Automatic Speech Recognition Using Data Augmentation by Bartelds, Martijn, San, Nay, McDonnell, Bradley, Jurafsky, Dan, Wieling, Martijn

“…The performance of automatic speech recognition (ASR) systems has advanced substantially in recent years, particularly for languages for which a large amount…”
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Adapting Monolingual Models: Data can be Scarce when Language Similarity is High by de Vries, Wietse, Bartelds, Martijn, Nissim, Malvina, Wieling, Martijn

“…Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021 For many (minority) languages, the resources needed to train large models are not…”
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ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets by Shi, Jiatong, Wang, Shih-Heng, Chen, William, Bartelds, Martijn, Kumar, Vanya Bannihatti, Tian, Jinchuan, Chang, Xuankai, Jurafsky, Dan, Livescu, Karen, Lee, Hung-yi, Watanabe, Shinji

“…ML-SUPERB evaluates self-supervised learning (SSL) models on the tasks of language identification and automatic speech recognition (ASR). This benchmark treats…”
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Leveraging Pre-Trained Representations to Improve Access to Untranscribed Speech from Endangered Languages by San, Nay, Bartelds, Martijn, Browne, Mitchell, Clifford, Lily, Gibson, Fiona, Mansfield, John, Nash, David, Simpson, Jane, Turpin, Myfany, Vollmer, Maria, Wilmoth, Sasha, Jurafsky, Dan

“…Pre-trained speech representations like wav2vec 2.0 are a powerful tool for automatic speech recognition (ASR). Yet many endangered languages lack sufficient…”
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Automated speech tools for helping communities process restricted-access corpora for language revival efforts by San, Nay, Bartelds, Martijn, Ògúnrèmí, Tolúlopé, Mount, Alison, Thompson, Ruben, Higgins, Michael, Barker, Roy, Simpson, Jane, Jurafsky, Dan

“…Many archival recordings of speech from endangered languages remain unannotated and inaccessible to community members and language learning programs. One…”
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Leveraging supplementary text data to kick-start automatic speech recognition system development with limited transcriptions by San, Nay, Bartelds, Martijn, Billings, Blaine, de Falco, Ella, Feriza, Hendi, Safri, Johan, Sahrozi, Wawan, Foley, Ben, McDonnell, Bradley, Jurafsky, Dan

“…Recent research using pre-trained transformer models suggests that just 10 minutes of transcribed speech may be enough to fine-tune such a model for automatic…”
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Neural Representations for Modeling Variation in Speech by Bartelds, Martijn, de Vries, Wietse, Sanal, Faraz, Richter, Caitlin, Liberman, Mark, Wieling, Martijn

“…Variation in speech is often quantified by comparing phonetic transcriptions of the same utterance. However, manually transcribing speech is time-consuming and…”
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Leveraging pre-trained representations to improve access to untranscribed speech from endangered languages by San, Nay, Bartelds, Martijn, Browne, Mitchell, Clifford, Lily, Gibson, Fiona, Mansfield, John, Nash, David, Simpson, Jane, Turpin, Myfany, Vollmer, Maria, Wilmoth, Sasha, Jurafsky, Dan

“…Pre-trained speech representations like wav2vec 2.0 are a powerful tool for automatic speech recognition (ASR). Yet many endangered languages lack sufficient…”
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