Search Results for author:
Found 11 papers, 1 papers with code
Corpus Synthesis for Zero-shot ASR domain Adaptation using Large Language Models
In this paper, we propose a new strategy for adapting ASR models to new target domains without any text or speech from those domains.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Text is All You Need: Personalizing ASR Models using Controllable Speech Synthesis
Here, we ask two fundamental questions about this strategy: when is synthetic data effective for personalization, and why is it effective in those cases?
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
I see what you hear: a vision-inspired method to localize words
This paper explores the possibility of using visual object detection techniques for word localization in speech data.
Synt++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition
With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Subspace Representation Learning for Few-shot Image Classification
In this paper, we propose a subspace representation learning (SRL) framework to tackle few-shot image classification tasks.
Pose Guided Person Image Generation with Hidden p-Norm Regression
In this paper, we propose a novel approach to solve the pose guided person image generation task.
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation
Our policy adapts the augmentation parameters based on the training loss of the data samples.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Project RISE: Recognizing Industrial Smoke Emissions
Industrial smoke emissions pose a significant concern to human health.
Unsupervised Style and Content Separation by Minimizing Mutual Information for Speech Synthesis
We present a method to generate speech from input text and a style vector that is extracted from a reference speech signal in an unsupervised manner, i. e., no style annotation, such as speaker information, is required.
Multi-shot Person Re-identification through Set Distance with Visual Distributional Representation
In this work, we propose the idea of visual distributional representation, which interprets an image set as samples drawn from an unknown distribution in appearance feature space.
Complex spectrogram enhancement by convolutional neural network with multi-metrics learning
This paper aims to address two issues existing in the current speech enhancement methods: 1) the difficulty of phase estimations; 2) a single objective function cannot consider multiple metrics simultaneously.
