Search Results for author: Oleg Rybakov

Found 7 papers, 3 papers with code

4-bit Conformer with Native Quantization Aware Training for Speech Recognition

no code implementations29 Mar 2022 Shaojin Ding, Phoenix Meadowlark, Yanzhang He, Lukasz Lew, Shivani Agrawal, Oleg Rybakov

Reducing the latency and model size has always been a significant research problem for live Automatic Speech Recognition (ASR) application scenarios.

Automatic Speech Recognition Quantization

A Scalable Model Specialization Framework for Training and Inference using Submodels and its Application to Speech Model Personalization

no code implementations23 Mar 2022 Fadi Biadsy, Youzheng Chen, Xia Zhang, Oleg Rybakov, Andrew Rosenberg, Pedro J. Moreno

We also show that learning a speaker-embedding space can scale further and reduce the amount of personalization training data required per speaker.

Real time spectrogram inversion on mobile phone

1 code implementation1 Mar 2022 Oleg Rybakov, Marco Tagliasacchi, Yunpeng Li, Liyang Jiang, Xia Zhang, Fadi Biadsy

In this paper, we focus on methods for real time spectrogram inversion, where an algorithm receives a portion of the input signal (e. g., one frame) and processes it incrementally, i. e., operating in streaming mode.


Pareto-Optimal Quantized ResNet Is Mostly 4-bit

1 code implementation7 May 2021 Amirali Abdolrashidi, Lisa Wang, Shivani Agrawal, Jonathan Malmaud, Oleg Rybakov, Chas Leichner, Lukasz Lew

In this work, we use ResNet as a case study to systematically investigate the effects of quantization on inference compute cost-quality tradeoff curves.


Streaming keyword spotting on mobile devices

3 code implementations14 May 2020 Oleg Rybakov, Natasha Kononenko, Niranjan Subrahmanya, Mirko Visontai, Stella Laurenzo

In this work we explore the latency and accuracy of keyword spotting (KWS) models in streaming and non-streaming modes on mobile phones.

Audio and Speech Processing Sound


no code implementations ICLR 2018 Oleg Rybakov, Vijai Mohan, Avishkar Misra, Scott LeGrand, Rejith Joseph, Kiuk Chung, Siddharth Singh, Qian You, Eric Nalisnick, Leo Dirac, Runfei Luo

We present a personalized recommender system using neural network for recommending products, such as eBooks, audio-books, Mobile Apps, Video and Music.

Recommendation Systems

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