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Bowen Shi

27 papers hereh-index 172.8k citations43 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • sole author1
  • first author14
  • middle author11
  • last author1

Across the 27 of 27 papers where every author was matched, so the position is known.

fields
  • cs.CL9
  • eess.AS9
  • cs.CV6
  • cs.AI1
  • cs.LG1
  • cs.SD1
same name
  • Bowen Shi — 23 papers, h 13
  • Bowen Shi — 17 papers, h 12
  • Bowen Shi — 12 papers, h 10
  • Bowen Shi — 4 papers, h 5
  • Bowen Shi — 4 papers, h 4
  • Bowen Shi — 3 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20172023
most citedScaling Speech Technology to 1,000+ Languages

116 citations · 334 across the 26 of their papers we have counts for

collaborators
Showing 2020Show all

4 papers · 1 filter

eess.AS2020★ 1 cited

A Joint Framework for Audio Tagging and Weakly Supervised Acoustic Event Detection Using DenseNet with Global Average Pooling

Chieh-Chi Kao, Bowen Shi, Ming Sun +1

This paper proposes a network architecture mainly designed for audio tagging, which can also be used for weakly supervised acoustic event detection (AED). The proposed network cons…

eess.AS2020

Whole-Word Segmental Speech Recognition with Acoustic Word Embeddings

Bowen Shi, Shane Settle, Karen Livescu

Segmental models are sequence prediction models in which scores of hypotheses are based on entire variable-length segments of frames. We consider segmental models for whole-word ("…

cs.CL2020

A Cross-Task Analysis of Text Span Representations

Shubham Toshniwal, Haoyue Shi, Bowen Shi +3

Many natural language processing (NLP) tasks involve reasoning with textual spans, including question answering, entity recognition, and coreference resolution. While extensive res…

cs.LG2020

Few-shot acoustic event detection via meta-learning

Bowen Shi, Ming Sun, Krishna C. Puvvada +3

We study few-shot acoustic event detection (AED) in this paper. Few-shot learning enables detection of new events with very limited labeled data. Compared to other research areas l…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.