Search Results for author:
Found 14 papers, 7 papers with code
Handling the Alignment for Wake Word Detection: A Comparison Between Alignment-Based, Alignment-Free and Hybrid Approaches
The third, proposed by us, is a hybrid solution in which the model is trained with a small set of aligned data and then tuned with a sizeable unaligned dataset.
LiCo-Net: Linearized Convolution Network for Hardware-efficient Keyword Spotting
This paper proposes a hardware-efficient architecture, Linearized Convolution Network (LiCo-Net) for keyword spotting.
Self-Supervised Speaker Verification with Simple Siamese Network and Self-Supervised Regularization
With our strong online data augmentation strategy, the proposed SSReg shows the potential of self-supervised learning without using negative pairs and it can significantly improve the performance of self-supervised speaker representation learning with a simple Siamese network architecture.
Signal Combination for Language Identification
We compare the performance of a lattice-based ensemble model and a deep neural network model to combine signals from recognizers with that of a baseline that only uses low-level acoustic signals.
Personal VAD: Speaker-Conditioned Voice Activity Detection
In this paper, we propose "personal VAD", a system to detect the voice activity of a target speaker at the frame level.
Tuplemax Loss for Language Identification
In many scenarios of a language identification task, the user will specify a small set of languages which he/she can speak instead of a large set of all possible languages.
Links: A High-Dimensional Online Clustering Method
We present a novel algorithm, called Links, designed to perform online clustering on unit vectors in a high-dimensional Euclidean space.
Generalized End-to-End Loss for Speaker Verification
In this paper, we propose a new loss function called generalized end-to-end (GE2E) loss, which makes the training of speaker verification models more efficient than our previous tuple-based end-to-end (TE2E) loss function.
Ranked #1 on
Speaker Verification
on CALLHOME
Attention-Based Models for Text-Dependent Speaker Verification
Attention-based models have recently shown great performance on a range of tasks, such as speech recognition, machine translation, and image captioning due to their ability to summarize relevant information that expands through the entire length of an input sequence.
Speaker Diarization with LSTM
For many years, i-vector based audio embedding techniques were the dominant approach for speaker verification and speaker diarization applications.
Ranked #1 on
Speaker Diarization
on CALLHOME-109
End-to-End Integration of a Convolution Network, Deformable Parts Model and Non-Maximum Suppression
In this paper, we propose a new model that combines these two approaches, obtaining the advantages of each.
End-to-End Integration of a Convolutional Network, Deformable Parts Model and Non-Maximum Suppression
In this paper, we propose a new model that combines these two approaches, obtaining the advantages of each.
Regularization of Neural Networks using DropConnect
When training with Dropout, a randomly selected subset of activations are set to zero within each layer.
Ranked #7 on
Image Classification
on MNIST
