Papers by Matthew Wiesner
Where are you from? Geolocating Speech and Applications to Language Identification (2024.naacl-long)
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Patrick Foley, Matthew Wiesner, Bismarck Odoom, Leibny Paola Garcia Perera, Kenton Murray, Philipp Koehn
| Challenge: | Language identification (LID) is a critical component in many modern multilingual speech technologies. |
| Approach: | They propose to use radio broadcasts with known origin to train regression models . they also propose to explore using geolocation as a proxy task for LID . |
| Outcome: | The proposed model outperforms pretrained models on the FLEURS benchmark and on the VoxLingua benchmark. |
Massively Multilingual Adversarial Speech Recognition (N19-1)
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| Challenge: | Prior work in multilingual and cross-lingual speech recognition has been limited to a subset of the world's most-spoken languages. |
| Approach: | They propose to use phonemes and phonemes as pretraining objectives to encourage language-independent representations. |
| Outcome: | The proposed model is able to learn language-independent representations of speech using multilingual training. |
End-to-end ASR to jointly predict transcriptions and linguistic annotations (2021.naacl-main)
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| Challenge: | Existing models generate audio transcripts by sequentially producing likely graphemes, or multi-graphemic units, from which lexical items of a language can be recovered. |
| Approach: | They propose a Transformer-based sequence-to-sequence model for automatic speech recognition that can produce high-quality transcriptions and linguistic annotations. |
| Outcome: | The proposed model can produce high-quality transcriptions and linguistic annotations on Japanese and English audio datasets. |
A Corpus for Large-Scale Phonetic Typology (2020.acl-main)
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Elizabeth Salesky, Eleanor Chodroff, Tiago Pimentel, Matthew Wiesner, Ryan Cotterell, Alan W Black, Jason Eisner
| Challenge: | Existing multilingual speech corpora have limited data in many languages . existing corpus is limited to a small number of languages with available data . |
| Approach: | They propose a large-scale phonetic typology corpus with phoneme-level labels and phoneme alignments in 690 readings spanning 635 languages. |
| Outcome: | The proposed corpus covers 635 languages and includes acoustic-phonetic measures of vowels and sibilants. |
Whisper-UT: A Unified Translation Framework for Speech and Text (2025.emnlp-main)
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Cihan Xiao, Matthew Wiesner, Debashish Chakraborty, Reno Kriz, Keith Cunningham, Kenton Murray, Kevin Duh, Luis Tavarez-Arce, Paul McNamee, Sanjeev Khudanpur
| Challenge: | Encoder-decoder models have achieved remarkable success in speech and text tasks, but efficiently adapting them to diverse uni/multimodal scenarios remains a challenge. |
| Approach: | They propose a framework that leverages lightweight adapters to enable seamless adaptation across tasks. |
| Outcome: | The proposed framework improves speech translation performance through a 2-stage decoding strategy without requiring 3-way parallel data. |