activity
20162023
most citedTime-domain speaker extraction network

36 citations · 123 across the 43 of their papers we have counts for

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Showing 2020Show all

9 papers · 1 filter

eess.AS2020★ 5 cited

Multi-stage Speaker Extraction with Utterance and Frame-Level Reference Signals

Meng Ge, Chenglin Xu, Longbiao Wang +3

Speaker extraction requires a sample speech from the target speaker as the reference. However, enrolling a speaker with a long speech is not practical. We propose a speaker extract…

cs.SD2020

Enriching Under-Represented Named-Entities To Improve Speech Recognition Performance

Tingzhi Mao, Yerbolat Khassanov, Van Tung Pham +4

Automatic speech recognition (ASR) for under-represented named-entity (UR-NE) is challenging due to such named-entities (NE) have insufficient instances and poor contextual coverag…

eess.AS2020

Multilingual Approach to Joint Speech and Accent Recognition with DNN-HMM Framework

Yizhou Peng, Jicheng Zhang, Haobo Zhang +3

Human can recognize speech, as well as the peculiar accent of the speech simultaneously. However, present state-of-the-art ASR system can rarely do that. In this paper, we propose…

cs.CL2020

Adapting BERT for Word Sense Disambiguation with Gloss Selection Objective and Example Sentences

Boon Peng Yap, Andrew Koh, Eng Siong Chng

Domain adaptation or transfer learning using pre-trained language models such as BERT has proven to be an effective approach for many natural language processing tasks. In this wor…

eess.AS2020★ 1 cited

Monolingual Data Selection Analysis for English-Mandarin Hybrid Code-switching Speech Recognition

Haobo Zhang, Haihua Xu, Van Tung Pham +2

In this paper, we conduct data selection analysis in building an English-Mandarin code-switching (CS) speech recognition (CSSR) system, which is aimed for a real CSSR contest in Ch…

eess.AS2020

Leveraging Text Data Using Hybrid Transformer-LSTM Based End-to-End ASR in Transfer Learning

Zhiping Zeng, Van Tung Pham, Haihua Xu +4

In this work, we study leveraging extra text data to improve low-resource end-to-end ASR under cross-lingual transfer learning setting. To this end, we extend our prior work [1], a…