164 citations · 188 across the 12 of their papers we have counts for
6 papers · 1 filter
Building Synthetic Speaker Profiles in Text-to-Speech Systems
Jie Pu, Yixiong Meng, Oguz Elibol
The diversity of speaker profiles in multi-speaker TTS systems is a crucial aspect of its performance, as it measures how many different speaker profiles TTS systems could possibly…
Scaling Laws for Acoustic Models
Jasha Droppo, Oguz Elibol
There is a recent trend in machine learning to increase model quality by growing models to sizes previously thought to be unreasonable. Recent work has shown that autoregressive ge…
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…