13 citations · 24 across the 10 of their papers we have counts for
7 papers · 1 filter
Do You Listen with One or Two Microphones? A Unified ASR Model for Single and Multi-Channel Audio
Gokce Keskin, Minhua Wu, Brian King +5
Automatic speech recognition (ASR) models are typically designed to operate on a single input data type, e.g. a single or multi-channel audio streamed from a device. This design de…
Attention-based Neural Beamforming Layers for Multi-channel Speech Recognition
Bhargav Pulugundla, Yang Gao, Brian King +5
Attention-based beamformers have recently been shown to be effective for multi-channel speech recognition. However, they are less capable at capturing local information. In this wo…
REDAT: Accent-Invariant Representation for End-to-End ASR by Domain Adversarial Training with Relabeling
Hu Hu, Xuesong Yang, Zeynab Raeesy +6
Accents mismatching is a critical problem for end-to-end ASR. This paper aims to address this problem by building an accent-robust RNN-T system with domain adversarial training (DA…
Measuring the Effectiveness of Voice Conversion on Speaker Identification and Automatic Speech Recognition Systems
Gokce Keskin, Tyler Lee, Cory Stephenson +1
This paper evaluates the effectiveness of a Cycle-GAN based voice converter (VC) on four speaker identification (SID) systems and an automated speech recognition (ASR) system for v…
Semi-supervised and Population Based Training for Voice Commands Recognition
Oguz H. Elibol, Gokce Keskin, Anil Thomas
We present a rapid design methodology that combines automated hyper-parameter tuning with semi-supervised training to build highly accurate and robust models for voice commands cla…
Adversarially Trained Autoencoders for Parallel-Data-Free Voice Conversion
Orhan Ocal, Oguz H. Elibol, Gokce Keskin +3
We present a method for converting the voices between a set of speakers. Our method is based on training multiple autoencoder paths, where there is a single speaker-independent enc…
Many-to-Many Voice Conversion with Out-of-Dataset Speaker Support
Gokce Keskin, Tyler Lee, Cory Stephenson +1
We present a Cycle-GAN based many-to-many voice conversion method that can convert between speakers that are not in the training set. This property is enabled through speaker embed…