14 citations · 27 across the 8 of their papers we have counts for
8 papers
Latent Positional Information is in the Self-Attention Variance of Transformer Language Models Without Positional Embeddings
Ta-Chung Chi, Ting-Han Fan, Li-Wei Chen +2
The use of positional embeddings in transformer language models is widely accepted. However, recent research has called into question the necessity of such embeddings. We further e…
A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous Speech
Li-Wei Chen, Shinji Watanabe, Alexander Rudnicky
Recent Text-to-Speech (TTS) systems trained on reading or acted corpora have achieved near human-level naturalness. The diversity of human speech, however, often goes beyond the co…
Speaker-Independent Acoustic-to-Articulatory Speech Inversion
Peter Wu, Li-Wei Chen, Cheol Jun Cho +4
To build speech processing methods that can handle speech as naturally as humans, researchers have explored multiple ways of building an invertible mapping from speech to an interp…
A unified one-shot prosody and speaker conversion system with self-supervised discrete speech units
Li-Wei Chen, Shinji Watanabe, Alexander Rudnicky
We present a unified system to realize one-shot voice conversion (VC) on the pitch, rhythm, and speaker attributes. Existing works generally ignore the correlation between prosody…
Exploring Wav2vec 2.0 fine-tuning for improved speech emotion recognition
Li-Wei Chen, Alexander Rudnicky
While Wav2Vec 2.0 has been proposed for speech recognition (ASR), it can also be used for speech emotion recognition (SER); its performance can be significantly improved using diff…
Fine-grained style control in Transformer-based Text-to-speech Synthesis
Li-Wei Chen, Alexander Rudnicky
In this paper, we present a novel architecture to realize fine-grained style control on the transformer-based text-to-speech synthesis (TransformerTTS). Specifically, we model the…
Speech Representation Learning Combining Conformer CPC with Deep Cluster for the ZeroSpeech Challenge 2021
Takashi Maekaku, Xuankai Chang, Yuya Fujita +3
We present a system for the Zero Resource Speech Challenge 2021, which combines a Contrastive Predictive Coding (CPC) with deep cluster. In deep cluster, we first prepare pseudo-la…
Generative Adversarial Networks for Unpaired Voice Transformation on Impaired Speech
Li-Wei Chen, Hung-Yi Lee, Yu Tsao
This paper focuses on using voice conversion (VC) to improve the speech intelligibility of surgical patients who have had parts of their articulators removed. Due to the difficulty…