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Zhengkun Tian

6 accepted papers

2023

Peak-First CTC: Reducing the Peak Latency of CTC Models by Applying Peak-First Regularization

ICASSP 2023accepted

The CTC model has been widely applied to many application scenarios because of its simple structure, excellent performance, and fast inference speed. There are many peaks in the probability distribution predicted by the CTC models, and each peak represents a non-blank token. The recognition latency…

Cited by 0SourceScholar
2022

ADD 2022: the first Audio Deep Synthesis Detection Challenge

ICASSP 2022accepted

Audio deepfake detection is an emerging topic, which was included in the ASVspoof 2021. However, the recent shared tasks have not covered many real-life and challenging scenarios. The first Audio Deep synthesis Detection challenge (ADD) was motivated to fill in the gap. The ADD 2022 includes three t…

Cited by 0SourceScholar
2022

End-to-End Network Based on Transformer for Automatic Detection of Covid-19

ICASSP 2022accepted

The novel coronavirus disease (COVID-19) was declared a pandemic by the World Health Organization. The cumulative number of deaths is more than 4.8 million. Epidemiology experts concur that mass testing is essential for isolating infected individuals, contact tracing, and slowing the progression of…

Cited by 0SourceScholar
2021

A Large-Scale Chinese Multimodal NER Dataset with Speech Clues

ACL 2021long

In this paper, we aim to explore an uncharted territory, which is Chinese multimodal named entity recognition (NER) with both textual and acoustic contents. To achieve this, we construct a large-scale human-annotated Chinese multimodal NER dataset, named CNERTA. Our corpus totally contains 42,987 an…

2021

Decoupling Pronunciation and Language for End-to-End Code-Switching Automatic Speech Recognition

ICASSP 2021accepted

Despite the recent significant advances witnessed in end-to-end (E2E) ASR system for code-switching, hunger for audio-text paired data limits the further improvement of the models’ performance. In this paper, we propose a decoupled transformer model to use mono-lingual paired data and unpaired text…

Cited by 0SourceScholar
2020

Synchronous Transformers for end-to-end Speech Recognition

ICASSP 2020accepted

For most of the attention-based sequence-to-sequence models, the decoder predicts the output sequence conditioned on the entire input sequence processed by the encoder. The asynchronous problem between the encoding and decoding makes these models difficult to be applied for online speech recognition…

Cited by 0SourceScholar