Search Results for author: Gokce Keskin

Found 8 papers, 0 papers with code

Do You Listen with One or Two Microphones? A Unified ASR Model for Single and Multi-Channel Audio

no code implementations4 Jun 2021 Gokce Keskin, Minhua Wu, Brian King, Harish Mallidi, Yang Gao, Jasha Droppo, Ariya Rastrow, Roland Maas

An ASR model that operates on both primary and auxiliary data can achieve better accuracy compared to a primary-only solution; and a model that can serve both primary-only (PO) and primary-plus-auxiliary (PPA) modes is highly desirable.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +1

Semi-supervised voice conversion with amortized variational inference

no code implementations30 Sep 2019 Cory Stephenson, Gokce Keskin, Anil Thomas, Oguz H. Elibol

In this work we introduce a semi-supervised approach to the voice conversion problem, in which speech from a source speaker is converted into speech of a target speaker.

Variational Inference Voice Conversion

Improving Branch Prediction By Modeling Global History with Convolutional Neural Networks

no code implementations20 Jun 2019 Stephen J Tarsa, Chit-Kwan Lin, Gokce Keskin, Gautham Chinya, Hong Wang

CPU branch prediction has hit a wall--existing techniques achieve near-perfect accuracy on 99% of static branches, and yet the mispredictions that remain hide major performance gains.

Semi-supervised and Population Based Training for Voice Commands Recognition

no code implementations10 May 2019 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 classification.

Classification General Classification +1

Adversarially Trained Autoencoders for Parallel-Data-Free Voice Conversion

no code implementations9 May 2019 Orhan Ocal, Oguz H. Elibol, Gokce Keskin, Cory Stephenson, Anil Thomas, Kannan Ramchandran

Due to the use of a single encoder, our method can generalize to converting the voice of out-of-training speakers to speakers in the training dataset.

Voice Conversion

Many-to-Many Voice Conversion with Out-of-Dataset Speaker Support

no code implementations30 Apr 2019 Gokce Keskin, Tyler Lee, Cory Stephenson, Oguz H. Elibol

We present a Cycle-GAN based many-to-many voice conversion method that can convert between speakers that are not in the training set.

Speaker Identification Voice Conversion

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