Search Results for author:
Found 13 papers, 1 papers with code
VoiceShop: A Unified Speech-to-Speech Framework for Identity-Preserving Zero-Shot Voice Editing
We present VoiceShop, a novel speech-to-speech framework that can modify multiple attributes of speech, such as age, gender, accent, and speech style, in a single forward pass while preserving the input speaker's timbre.
Language-universal phonetic encoder for low-resource speech recognition
Our main approach and adaptation are effective on extremely low-resource languages, even within domain- and language-mismatched scenarios.
Language-Universal Phonetic Representation in Multilingual Speech Pretraining for Low-Resource Speech Recognition
Moreover, on 3 of the 4 languages, comparing to the standard HuBERT, the approach performs better, meanwhile is able to save supervised training data by 1. 5k hours (75%) at most.
Memory Augmented Lookup Dictionary based Language Modeling for Automatic Speech Recognition
Recent studies have shown that using an external Language Model (LM) benefits the end-to-end Automatic Speech Recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Streaming Voice Conversion Via Intermediate Bottleneck Features And Non-streaming Teacher Guidance
In this paper, we propose to use intermediate bottleneck features (IBFs) to replace PPGs.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Cloning one's voice using very limited data in the wild
(2) How to clone a person's voice while controlling the style and prosody.
Graph Sequential Network for Reasoning over Sequences
We validate the proposed GSN on two NLP tasks: interpretable multi-hop reading comprehension on HotpotQA and graph based fact verification on FEVER.
Speaker-invariant Affective Representation Learning via Adversarial Training
In this paper, we propose a machine learning framework to obtain speech emotion representations by limiting the effect of speaker variability in the speech signals.
Select, Answer and Explain: Interpretable Multi-hop Reading Comprehension over Multiple Documents
Interpretable multi-hop reading comprehension (RC) over multiple documents is a challenging problem because it demands reasoning over multiple information sources and explaining the answer prediction by providing supporting evidences.
Multiple instance learning with graph neural networks
In this paper, we propose a new end-to-end graph neural network (GNN) based algorithm for MIL: we treat each bag as a graph and use GNN to learn the bag embedding, in order to explore the useful structural information among instances in bags.
Multi-hop Reading Comprehension across Multiple Documents by Reasoning over Heterogeneous Graphs
We introduce a heterogeneous graph with different types of nodes and edges, which is named as Heterogeneous Document-Entity (HDE) graph.
I4U Submission to NIST SRE 2018: Leveraging from a Decade of Shared Experiences
The I4U consortium was established to facilitate a joint entry to NIST speaker recognition evaluations (SRE).
Reducing the Model Order of Deep Neural Networks Using Information Theory
In this paper, we propose a method to compress deep neural networks by using the Fisher Information metric, which we estimate through a stochastic optimization method that keeps track of second-order information in the network.
