Search Results for author:
Found 19 papers, 4 papers with code
Learning Phone Recognition from Unpaired Audio and Phone Sequences Based on Generative Adversarial Network
GAN training is adopted in the first stage to find the mapping relationship between unpaired speech and phone sequence.
Meta-TTS: Meta-Learning for Few-Shot Speaker Adaptive Text-to-Speech
On the one hand, speaker adaptation methods fine-tune a trained multi-speaker text-to-speech (TTS) model with few enrolled samples.
Speech Representation Learning Through Self-supervised Pretraining And Multi-task Finetuning
It has been shown that an SSL pretraining model can achieve excellent performance in various downstream tasks of speech processing.
SpeechNet: A Universal Modularized Model for Speech Processing Tasks
The multi-task learning of a wide variety of speech processing tasks with a universal model has not been studied.
Stabilizing Label Assignment for Speech Separation by Self-supervised Pre-training
Speech separation has been well developed, with the very successful permutation invariant training (PIT) approach, although the frequent label assignment switching happening during PIT training remains to be a problem when better convergence speed and achievable performance are desired.
Ranked #6 on
Speech Separation
on Libri2Mix
(using extra training data)
DARTS-ASR: Differentiable Architecture Search for Multilingual Speech Recognition and Adaptation
In order to examine the generalizability of DARTS-ASR, we apply our approach not only on many languages to perform monolingual ASR, but also on a multilingual ASR setting.
AIPNet: Generative Adversarial Pre-training of Accent-invariant Networks for End-to-end Speech Recognition
As one of the major sources in speech variability, accents have posed a grand challenge to the robustness of speech recognition systems.
From Semi-supervised to Almost-unsupervised Speech Recognition with Very-low Resource by Jointly Learning Phonetic Structures from Audio and Text Embeddings
However, we note human babies start to learn the language by the sounds (or phonetic structures) of a small number of exemplar words, and "generalize" such knowledge to other words without hearing a large amount of data.
Improved Audio Embeddings by Adjacency-Based Clustering with Applications in Spoken Term Detection
Embedding audio signal segments into vectors with fixed dimensionality is attractive because all following processing will be easier and more efficient, for example modeling, classifying or indexing.
Almost-unsupervised Speech Recognition with Close-to-zero Resource Based on Phonetic Structures Learned from Very Small Unpaired Speech and Text Data
This can be learned by aligning a small number of spoken words and the corresponding text words in the embedding spaces.
Phonetic-and-Semantic Embedding of Spoken Words with Applications in Spoken Content Retrieval
Stage 1 performs phonetic embedding with speaker characteristics disentangled.
Scalable Bilinear Pi Learning Using State and Action Features
In this work, we study a primal-dual formulation of the ALP, and develop a scalable, model-free algorithm called bilinear $\pi$ learning for reinforcement learning when a sampling oracle is provided.
Scalable Bilinear $π$ Learning Using State and Action Features
In this work, we study a primal-dual formulation of the ALP, and develop a scalable, model-free algorithm called bilinear $\pi$ learning for reinforcement learning when a sampling oracle is provided.
Towards Unsupervised Automatic Speech Recognition Trained by Unaligned Speech and Text only
In this work, we propose a framework to achieve unsupervised ASR on a read English speech dataset, where audio and text are unaligned.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Order-Free RNN with Visual Attention for Multi-Label Classification
In this paper, we propose the joint learning attention and recurrent neural network (RNN) models for multi-label classification.
Lower Bound On the Computational Complexity of Discounted Markov Decision Problems
We study the computational complexity of the infinite-horizon discounted-reward Markov Decision Problem (MDP) with a finite state space $|\mathcal{S}|$ and a finite action space $|\mathcal{A}|$.
Stochastic Primal-Dual Methods and Sample Complexity of Reinforcement Learning
We study the online estimation of the optimal policy of a Markov decision process (MDP).
Random Multi-Constraint Projection: Stochastic Gradient Methods for Convex Optimization with Many Constraints
Consider convex optimization problems subject to a large number of constraints.
Dictionary Learning from Ambiguously Labeled Data
We propose a novel dictionary-based learning method for ambiguously labeled multiclass classification, where each training sample has multiple labels and only one of them is the correct label.
