Search Results for author:
Found 34 papers, 21 papers with code
A Comparative Study on Transformer vs RNN in Speech Applications
Sequence-to-sequence models have been widely used in end-to-end speech processing, for example, automatic speech recognition (ASR), speech translation (ST), and text-to-speech (TTS).
Ranked #12 on
Speech Recognition
on AISHELL-1
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
ESPnet-TTS: Unified, Reproducible, and Integratable Open Source End-to-End Text-to-Speech Toolkit
Furthermore, the unified design enables the integration of ASR functions with TTS, e. g., ASR-based objective evaluation and semi-supervised learning with both ASR and TTS models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
ESPnet-ST: All-in-One Speech Translation Toolkit
We present ESPnet-ST, which is designed for the quick development of speech-to-speech translation systems in a single framework.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
The Sequence-to-Sequence Baseline for the Voice Conversion Challenge 2020: Cascading ASR and TTS
This paper presents the sequence-to-sequence (seq2seq) baseline system for the voice conversion challenge (VCC) 2020.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
ESPnet2-TTS: Extending the Edge of TTS Research
This paper describes ESPnet2-TTS, an end-to-end text-to-speech (E2E-TTS) toolkit.
Unsupervised Data Selection for TTS: Using Arabic Broadcast News as a Case Study
Several high-resource Text to Speech (TTS) systems currently produce natural, well-established human-like speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
ESPnet-ST-v2: Multipurpose Spoken Language Translation Toolkit
ESPnet-ST-v2 is a revamp of the open-source ESPnet-ST toolkit necessitated by the broadening interests of the spoken language translation community.
S3PRL-VC: Open-source Voice Conversion Framework with Self-supervised Speech Representations
In this work, we provide a series of in-depth analyses by benchmarking on the two tasks in VCC2020, namely intra-/cross-lingual any-to-one (A2O) VC, as well as an any-to-any (A2A) setting.
crank: An Open-Source Software for Nonparallel Voice Conversion Based on Vector-Quantized Variational Autoencoder
In this paper, we present an open-source software for developing a nonparallel voice conversion (VC) system named crank.
ESPnet-ONNX: Bridging a Gap Between Research and Production
In the field of deep learning, researchers often focus on inventing novel neural network models and improving benchmarks.
A Comparative Study of Self-supervised Speech Representation Based Voice Conversion
We present a large-scale comparative study of self-supervised speech representation (S3R)-based voice conversion (VC).
Voice Transformer Network: Sequence-to-Sequence Voice Conversion Using Transformer with Text-to-Speech Pretraining
We introduce a novel sequence-to-sequence (seq2seq) voice conversion (VC) model based on the Transformer architecture with text-to-speech (TTS) pretraining.
Pretraining Techniques for Sequence-to-Sequence Voice Conversion
Sequence-to-sequence (seq2seq) voice conversion (VC) models are attractive owing to their ability to convert prosody.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Investigation of F0 conditioning and Fully Convolutional Networks in Variational Autoencoder based Voice Conversion
Such hypothesis implies that during the conversion phase, the latent codes and the converted features in VAE based VC are in fact source F0 dependent.
Non-Parallel Voice Conversion with Cyclic Variational Autoencoder
In this work, to overcome this problem, we propose to use CycleVAE-based spectral model that indirectly optimizes the conversion flow by recycling the converted features back into the system to obtain corresponding cyclic reconstructed spectra that can be directly optimized.
Quasi-Periodic Parallel WaveGAN Vocoder: A Non-autoregressive Pitch-dependent Dilated Convolution Model for Parametric Speech Generation
In this paper, we propose a parallel WaveGAN (PWG)-like neural vocoder with a quasi-periodic (QP) architecture to improve the pitch controllability of PWG.
Audio and Speech Processing Sound
Quasi-Periodic Parallel WaveGAN: A Non-autoregressive Raw Waveform Generative Model with Pitch-dependent Dilated Convolution Neural Network
To improve the pitch controllability and speech modeling capability, we apply a QP structure with PDCNNs to PWG, which introduces pitch information to the network by dynamically changing the network architecture corresponding to the auxiliary $F_{0}$ feature.
Quasi-Periodic WaveNet: An Autoregressive Raw Waveform Generative Model with Pitch-dependent Dilated Convolution Neural Network
In this paper, a pitch-adaptive waveform generative model named Quasi-Periodic WaveNet (QPNet) is proposed to improve the limited pitch controllability of vanilla WaveNet (WN) using pitch-dependent dilated convolution neural networks (PDCNNs).
Statistical Voice Conversion with Quasi-Periodic WaveNet Vocoder
However, because of the fixed dilated convolution and generic network architecture, the WN vocoder lacks robustness against unseen input features and often requires a huge network size to achieve acceptable speech quality.
Audio and Speech Processing Sound
Quasi-Periodic WaveNet Vocoder: A Pitch Dependent Dilated Convolution Model for Parametric Speech Generation
In this paper, we propose a quasi-periodic neural network (QPNet) vocoder with a novel network architecture named pitch-dependent dilated convolution (PDCNN) to improve the pitch controllability of WaveNet (WN) vocoder.
ViCE: Improving Dense Representation Learning by Superpixelization and Contrasting Cluster Assignment
Recent self-supervised models have demonstrated equal or better performance than supervised methods, opening for AI systems to learn visual representations from practically unlimited data.
Multi-Head Decoder for End-to-End Speech Recognition
This paper presents a new network architecture called multi-head decoder for end-to-end speech recognition as an extension of a multi-head attention model.
ESPnet: End-to-End Speech Processing Toolkit
This paper introduces a new open source platform for end-to-end speech processing named ESPnet.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Back-Translation-Style Data Augmentation for End-to-End ASR
In this paper we propose a novel data augmentation method for attention-based end-to-end automatic speech recognition (E2E-ASR), utilizing a large amount of text which is not paired with speech signals.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+4
Cycle-consistency training for end-to-end speech recognition
To solve this problem, this work presents a loss that is based on the speech encoder state sequence instead of the raw speech signal.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Refined WaveNet Vocoder for Variational Autoencoder Based Voice Conversion
Conventional WaveNet vocoders are trained with natural acoustic features but conditioned on the converted features in the conversion stage for VC, and such a mismatch often causes significant quality and similarity degradation.
End-to-End Automatic Speech Recognition Integrated With CTC-Based Voice Activity Detection
The proposed method is publicly available.
DiscreTalk: Text-to-Speech as a Machine Translation Problem
This paper proposes a new end-to-end text-to-speech (E2E-TTS) model based on neural machine translation (NMT).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Any-to-One Sequence-to-Sequence Voice Conversion using Self-Supervised Discrete Speech Representations
Given a training dataset of the target speaker, we extract VQW2V and acoustic features to estimate a seq2seq mapping function from the former to the latter.
Non-autoregressive sequence-to-sequence voice conversion
This paper proposes a novel voice conversion (VC) method based on non-autoregressive sequence-to-sequence (NAR-S2S) models.
Anomalous Sound Detection Using a Binary Classification Model and Class Centroids
Our results showed that multi-task learning using binary classification and metric learning to consider the distance from each class centroid in the feature space is effective, and performance can be significantly improved by using even a small amount of anomalous data during training.
On Prosody Modeling for ASR+TTS based Voice Conversion
In voice conversion (VC), an approach showing promising results in the latest voice conversion challenge (VCC) 2020 is to first use an automatic speech recognition (ASR) model to transcribe the source speech into the underlying linguistic contents; these are then used as input by a text-to-speech (TTS) system to generate the converted speech.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Discretization and Re-synthesis: an alternative method to solve the Cocktail Party Problem
Specifically, we propose a novel speech separation/enhancement model based on the recognition of discrete symbols, and convert the paradigm of the speech separation/enhancement related tasks from regression to classification.
Acoustic Event Detection with Classifier Chains
In each iteration, the event's activity is estimated and used to condition the next output based on the probabilistic chain rule to form classifier chains.
