54 citations · 148 across the 12 of their papers we have counts for
12 papers
Learning Phone Recognition from Unpaired Audio and Phone Sequences Based on Generative Adversarial Network
Da-rong Liu, Po-chun Hsu, Yi-chen Chen +4
ASR has been shown to achieve great performance recently. However, most of them rely on massive paired data, which is not feasible for low-resource languages worldwide. This paper…
Parallel Synthesis for Autoregressive Speech Generation
Po-chun Hsu, Da-rong Liu, Andy T. Liu +1
Autoregressive neural vocoders have achieved outstanding performance in speech synthesis tasks such as text-to-speech and voice conversion. An autoregressive vocoder predicts a sam…
Universal Adaptor: Converting Mel-Spectrograms Between Different Configurations for Speech Synthesis
Fan-Lin Wang, Po-chun Hsu, Da-rong Liu +1
Most recent speech synthesis systems are composed of a synthesizer and a vocoder. However, the existing synthesizers and vocoders can only be matched to acoustic features extracted…
Meta-TTS: Meta-Learning for Few-Shot Speaker Adaptive Text-to-Speech
Sung-Feng Huang, Chyi-Jiunn Lin, Da-Rong Liu +2
Personalizing a speech synthesis system is a highly desired application, where the system can generate speech with the user's voice with rare enrolled recordings. There are two mai…
Analyzing the Robustness of Unsupervised Speech Recognition
Guan-Ting Lin, Chan-Jan Hsu, Da-Rong Liu +2
Unsupervised speech recognition (unsupervised ASR) aims to learn the ASR system with non-parallel speech and text corpus only. Wav2vec-U has shown promising results in unsupervised…
SpeechNet: A Universal Modularized Model for Speech Processing Tasks
Yi-Chen Chen, Po-Han Chi, Shu-wen Yang +7
There is a wide variety of speech processing tasks ranging from extracting content information from speech signals to generating speech signals. For different tasks, model networks…
SUPERB: Speech processing Universal PERformance Benchmark
Shu-wen Yang, Po-Han Chi, Yung-Sung Chuang +17
Self-supervised learning (SSL) has proven vital for advancing research in natural language processing (NLP) and computer vision (CV). The paradigm pretrains a shared model on large…
Stabilizing Label Assignment for Speech Separation by Self-supervised Pre-training
Sung-Feng Huang, Shun-Po Chuang, Da-Rong Liu +3
Speech separation has been well developed, with the very successful permutation invariant training (PIT) approach, although the frequent label assignment switching happening during…
Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model
Da-Rong Liu, Chunxi Liu, Frank Zhang +3
Videos uploaded on social media are often accompanied with textual descriptions. In building automatic speech recognition (ASR) systems for videos, we can exploit the contextual in…
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…
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…
Attention-based Memory Selection Recurrent Network for Language Modeling
Da-Rong Liu, Shun-Po Chuang, Hung-yi Lee
Recurrent neural networks (RNNs) have achieved great success in language modeling. However, since the RNNs have fixed size of memory, their memory cannot store all the information…