3.6k citations · 6.7k across the 72 of their papers we have counts for
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Miipher: A Robust Speech Restoration Model Integrating Self-Supervised Speech and Text Representations
Yuma Koizumi, Heiga Zen, Shigeki Karita +7
Speech restoration (SR) is a task of converting degraded speech signals into high-quality ones. In this study, we propose a robust SR model called Miipher, and apply Miipher to a n…
Residual Adapters for Few-Shot Text-to-Speech Speaker Adaptation
Nobuyuki Morioka, Heiga Zen, Nanxin Chen +2
Adapting a neural text-to-speech (TTS) model to a target speaker typically involves fine-tuning most if not all of the parameters of a pretrained multi-speaker backbone model. Howe…
Virtuoso: Massive Multilingual Speech-Text Joint Semi-Supervised Learning for Text-To-Speech
Takaaki Saeki, Heiga Zen, Zhehuai Chen +6
This paper proposes Virtuoso, a massively multilingual speech-text joint semi-supervised learning framework for text-to-speech synthesis (TTS) models. Existing multilingual TTS typ…
Training Text-To-Speech Systems From Synthetic Data: A Practical Approach For Accent Transfer Tasks
Lev Finkelstein, Heiga Zen, Norman Casagrande +9
Transfer tasks in text-to-speech (TTS) synthesis - where one or more aspects of the speech of one set of speakers is transferred to another set of speakers that do not feature thes…
Improving Distortion Robustness of Self-supervised Speech Processing Tasks with Domain Adaptation
Kuan Po Huang, Yu-Kuan Fu, Yu Zhang +1
Speech distortions are a long-standing problem that degrades the performance of supervisely trained speech processing models. It is high time that we enhance the robustness of spee…
Ask2Mask: Guided Data Selection for Masked Speech Modeling
Murali Karthick Baskar, Andrew Rosenberg, Bhuvana Ramabhadran +2
Masked speech modeling (MSM) methods such as wav2vec2 or w2v-BERT learn representations over speech frames which are randomly masked within an utterance. While these methods improv…
Universal Paralinguistic Speech Representations Using Self-Supervised Conformers
Joel Shor, Aren Jansen, Wei Han +2
Many speech applications require understanding aspects beyond the words being spoken, such as recognizing emotion, detecting whether the speaker is wearing a mask, or distinguishin…
Parallel Tacotron 2: A Non-Autoregressive Neural TTS Model with Differentiable Duration Modeling
Isaac Elias, Heiga Zen, Jonathan Shen +4
This paper introduces Parallel Tacotron 2, a non-autoregressive neural text-to-speech model with a fully differentiable duration model which does not require supervised duration si…
Parallel Tacotron: Non-Autoregressive and Controllable TTS
Isaac Elias, Heiga Zen, Jonathan Shen +4
Although neural end-to-end text-to-speech models can synthesize highly natural speech, there is still room for improvements to its efficiency and naturalness. This paper proposes a…
Improving Streaming Automatic Speech Recognition With Non-Streaming Model Distillation On Unsupervised Data
Thibault Doutre, Wei Han, Min Ma +7
Streaming end-to-end automatic speech recognition (ASR) models are widely used on smart speakers and on-device applications. Since these models are expected to transcribe speech wi…
Non-Attentive Tacotron: Robust and Controllable Neural TTS Synthesis Including Unsupervised Duration Modeling
Jonathan Shen, Ye Jia, Mike Chrzanowski +4
This paper presents Non-Attentive Tacotron based on the Tacotron 2 text-to-speech model, replacing the attention mechanism with an explicit duration predictor. This improves robust…
LibriTTS: A Corpus Derived from LibriSpeech for Text-to-Speech
Heiga Zen, Viet Dang, Rob Clark +5
This paper introduces a new speech corpus called "LibriTTS" designed for text-to-speech use. It is derived from the original audio and text materials of the LibriSpeech corpus, whi…