28 citations · 52 across the 9 of their papers we have counts for
9 papers
A Transformer-based Cross-modal Fusion Model with Adversarial Training for VQA Challenge 2021
Ke-Han Lu, Bo-Han Fang, Kuan-Yu Chen
In this paper, inspired by the successes of visionlanguage pre-trained models and the benefits from training with adversarial attacks, we present a novel transformerbased cross-mod…
Non-autoregressive Transformer-based End-to-end ASR using BERT
Fu-Hao Yu, Kuan-Yu Chen
Transformer-based models have led to significant innovation in classical and practical subjects as varied as speech processing, natural language processing, and computer vision. On…
Speech Recognition by Simply Fine-tuning BERT
Wen-Chin Huang, Chia-Hua Wu, Shang-Bao Luo +3
We propose a simple method for automatic speech recognition (ASR) by fine-tuning BERT, which is a language model (LM) trained on large-scale unlabeled text data and can generate ri…
Investigation of Sentiment Controllable Chatbot
Hung-yi Lee, Cheng-Hao Ho, Chien-Fu Lin +5
Conventional seq2seq chatbot models attempt only to find sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the outpu…
An Audio-enriched BERT-based Framework for Spoken Multiple-choice Question Answering
Chia-Chih Kuo, Shang-Bao Luo, Kuan-Yu Chen
In a spoken multiple-choice question answering (SMCQA) task, given a passage, a question, and multiple choices all in the form of speech, the machine needs to pick the correct choi…
A neural document language modeling framework for spoken document retrieval
Li-Phen Yen, Zhen-Yu Wu, Kuan-Yu Chen
Recent developments in deep learning have led to a significant innovation in various classic and practical subjects, including speech recognition, computer vision, question answeri…
Completely Unsupervised Speech Recognition By A Generative Adversarial Network Harmonized With Iteratively Refined Hidden Markov Models
Kuan-Yu Chen, Che-Ping Tsai, Da-Rong Liu +2
Producing a large annotated speech corpus for training ASR systems remains difficult for more than 95% of languages all over the world which are low-resourced, but collecting a rel…
Scalable Sentiment for Sequence-to-sequence Chatbot Response with Performance Analysis
Chih-Wei Lee, Yau-Shian Wang, Tsung-Yuan Hsu +3
Conventional seq2seq chatbot models only try to find the sentences with the highest probabilities conditioned on the input sequences, without considering the sentiment of the outpu…
Completely Unsupervised Phoneme Recognition by Adversarially Learning Mapping Relationships from Audio Embeddings
Da-Rong Liu, Kuan-Yu Chen, Hung-Yi Lee +1
Unsupervised discovery of acoustic tokens from audio corpora without annotation and learning vector representations for these tokens have been widely studied. Although these techni…