98 citations · 188 across the 17 of their papers we have counts for
17 papers
TO-Rawnet: Improving RawNet with TCN and Orthogonal Regularization for Fake Audio Detection
Chenglong Wang, Jiangyan Yi, Jianhua Tao +4
Current fake audio detection relies on hand-crafted features, which lose information during extraction. To overcome this, recent studies use direct feature extraction from raw audi…
Detection of Cross-Dataset Fake Audio Based on Prosodic and Pronunciation Features
Chenglong Wang, Jiangyan Yi, Jianhua Tao +3
Existing fake audio detection systems perform well in in-domain testing, but still face many challenges in out-of-domain testing. This is due to the mismatch between the training a…
A Sequence-to-Set Network for Nested Named Entity Recognition
Zeqi Tan, Yongliang Shen, Shuai Zhang +2
Named entity recognition (NER) is a widely studied task in natural language processing. Recently, a growing number of studies have focused on the nested NER. The span-based methods…
Listen As You Wish: Audio based Event Detection via Text-to-Audio Grounding in Smart Cities
Haoyu Tang, Yunxiao Wang, Jihua Zhu +4
With the development of internet of things technologies, tremendous sensor audio data has been produced, which poses great challenges to audio-based event detection in smart cities…
Locate and Label: A Two-stage Identifier for Nested Named Entity Recognition
Yongliang Shen, Xinyin Ma, Zeqi Tan +3
Named entity recognition (NER) is a well-studied task in natural language processing. Traditional NER research only deals with flat entities and ignores nested entities. The span-b…
FSR: Accelerating the Inference Process of Transducer-Based Models by Applying Fast-Skip Regularization
Zhengkun Tian, Jiangyan Yi, Ye Bai +3
Transducer-based models, such as RNN-Transducer and transformer-transducer, have achieved great success in speech recognition. A typical transducer model decodes the output sequenc…
TSNAT: Two-Step Non-Autoregressvie Transformer Models for Speech Recognition
Zhengkun Tian, Jiangyan Yi, Jianhua Tao +4
The autoregressive (AR) models, such as attention-based encoder-decoder models and RNN-Transducer, have achieved great success in speech recognition. They predict the output sequen…
Fast End-to-End Speech Recognition via Non-Autoregressive Models and Cross-Modal Knowledge Transferring from BERT
Ye Bai, Jiangyan Yi, Jianhua Tao +3
Attention-based encoder-decoder (AED) models have achieved promising performance in speech recognition. However, because the decoder predicts text tokens (such as characters or wor…
Decoupling Pronunciation and Language for End-to-end Code-switching Automatic Speech Recognition
Shuai Zhang, Jiangyan Yi, Zhengkun Tian +3
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'…
One In A Hundred: Select The Best Predicted Sequence from Numerous Candidates for Streaming Speech Recognition
Zhengkun Tian, Jiangyan Yi, Ye Bai +3
The RNN-Transducers and improved attention-based encoder-decoder models are widely applied to streaming speech recognition. Compared with these two end-to-end models, the CTC model…
TensorCoder: Dimension-Wise Attention via Tensor Representation for Natural Language Modeling
Shuai Zhang, Peng Zhang, Xindian Ma +3
Transformer has been widely-used in many Natural Language Processing (NLP) tasks and the scaled dot-product attention between tokens is a core module of Transformer. This attention…
Spike-Triggered Non-Autoregressive Transformer for End-to-End Speech Recognition
Zhengkun Tian, Jiangyan Yi, Jianhua Tao +3
Non-autoregressive transformer models have achieved extremely fast inference speed and comparable performance with autoregressive sequence-to-sequence models in neural machine tran…
Listen Attentively, and Spell Once: Whole Sentence Generation via a Non-Autoregressive Architecture for Low-Latency Speech Recognition
Ye Bai, Jiangyan Yi, Jianhua Tao +3
Although attention based end-to-end models have achieved promising performance in speech recognition, the multi-pass forward computation in beam-search increases inference time cos…
Rnn-transducer with language bias for end-to-end Mandarin-English code-switching speech recognition
Shuai Zhang, Jiangyan Yi, Zhengkun Tian +2
Recently, language identity information has been utilized to improve the performance of end-to-end code-switching (CS) speech recognition. However, previous works use an additional…
Synchronous Transformers for End-to-End Speech Recognition
Zhengkun Tian, Jiangyan Yi, Ye Bai +3
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 asynchr…
Integrating Knowledge into End-to-End Speech Recognition from External Text-Only Data
Ye Bai, Jiangyan Yi, Jianhua Tao +3
Attention-based encoder-decoder (AED) models have achieved promising performance in speech recognition. However, because of the end-to-end training, an AED model is usually trained…
A Tensorized Transformer for Language Modeling
Xindian Ma, Peng Zhang, Shuai Zhang +4
Latest development of neural models has connected the encoder and decoder through a self-attention mechanism. In particular, Transformer, which is solely based on self-attention, h…