5 citations · 25 across the 13 of their papers we have counts for
13 papers
Ordinal Regression via Binary Preference vs Simple Regression: Statistical and Experimental Perspectives
Bin Su, Shaoguang Mao, Frank Soong +1
Ordinal regression with anchored reference samples (ORARS) has been proposed for predicting the subjective Mean Opinion Score (MOS) of input stimuli automatically. The ORARS addres…
Transformer-S2A: Robust and Efficient Speech-to-Animation
Liyang Chen, Zhiyong Wu, Jun Ling +3
We propose a novel robust and efficient Speech-to-Animation (S2A) approach for synchronized facial animation generation in human-computer interaction. Compared with conventional ap…
An Approach to Mispronunciation Detection and Diagnosis with Acoustic, Phonetic and Linguistic (APL) Embeddings
Wenxuan Ye, Shaoguang Mao, Frank Soong +4
Many mispronunciation detection and diagnosis (MD&D) research approaches try to exploit both the acoustic and linguistic features as input. Yet the improvement of the performance i…
Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding
Yingmei Guo, Linjun Shou, Jian Pei +4
Lack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages. Although various data augmentation approaches have be…
Voting for the right answer: Adversarial defense for speaker verification
Haibin Wu, Yang Zhang, Zhiyong Wu +2
Automatic speaker verification (ASV) is a well developed technology for biometric identification, and has been ubiquitous implemented in security-critic applications, such as banki…
VAENAR-TTS: Variational Auto-Encoder based Non-AutoRegressive Text-to-Speech Synthesis
Hui Lu, Zhiyong Wu, Xixin Wu +4
This paper describes a variational auto-encoder based non-autoregressive text-to-speech (VAENAR-TTS) model. The autoregressive TTS (AR-TTS) models based on the sequence-to-sequence…
Adversarial Sample Detection for Speaker Verification by Neural Vocoders
Haibin Wu, Po-chun Hsu, Ji Gao +6
Automatic speaker verification (ASV), one of the most important technology for biometric identification, has been widely adopted in security-critical applications. However, ASV is…
Improving the Adversarial Robustness for Speaker Verification by Self-Supervised Learning
Haibin Wu, Xu Li, Andy T. Liu +3
Previous works have shown that automatic speaker verification (ASV) is seriously vulnerable to malicious spoofing attacks, such as replay, synthetic speech, and recently emerged ad…
PTeacher: a Computer-Aided Personalized Pronunciation Training System with Exaggerated Audio-Visual Corrective Feedback
Yaohua Bu, Tianyi Ma, Weijun Li +12
Second language (L2) English learners often find it difficult to improve their pronunciations due to the lack of expressive and personalized corrective feedback. In this paper, we…
Towards Multi-Scale Style Control for Expressive Speech Synthesis
Xiang Li, Changhe Song, Jingbei Li +3
This paper introduces a multi-scale speech style modeling method for end-to-end expressive speech synthesis. The proposed method employs a multi-scale reference encoder to extract…
The Multi-speaker Multi-style Voice Cloning Challenge 2021
Qicong Xie, Xiaohai Tian, Guanghou Liu +9
The Multi-speaker Multi-style Voice Cloning Challenge (M2VoC) aims to provide a common sizable dataset as well as a fair testbed for the benchmarking of the popular voice cloning t…
Improving pronunciation assessment via ordinal regression with anchored reference samples
Bin Su, Shaoguang Mao, Frank Soong +3
Sentence level pronunciation assessment is important for Computer Assisted Language Learning (CALL). Traditional speech pronunciation assessment, based on the Goodness of Pronuncia…
Non-Autoregressive Transformer ASR with CTC-Enhanced Decoder Input
Xingchen Song, Zhiyong Wu, Yiheng Huang +3
Non-autoregressive (NAR) transformer models have achieved significantly inference speedup but at the cost of inferior accuracy compared to autoregressive (AR) models in automatic s…