Deepak Baby

Deepak Baby

Applied Scientist

Amazon Alexa


Deepak Baby is with the Amazon Alexa team as an Applied Scientist since October 2020.

He was previously working as a post-doctoral researcher with Prof. Hervé Bourlard at Idiap Research Institute between May 2019 and June 2020. Prior to that he was a post-doctoral researcher with Prof. Sarah Verhulst at WAVES, Department of Information Technology, Ghent University between February 2017 and April 2019, where he was working on DNN-based hearing restoration strategies for hearing impairment.

Deepak Baby received his PhD in Electrical Engineering (Thesis title : Non-negative Sparse Representations for Speech Enhancement and Recognition) from ESAT/PSI group in KU Leuven in November 2016 under the supervision of Prof. Hugo Van hamme. Deepak received the Masters degree in Communication Engineering from Dept. of Electrical Engineering, Indian Institute of Technology Bombay in 2012 and Bachelors degree in Electronics and Communication Engineering from College of Engineering Trivandrum, India in 2009.


  • Speech Recognition
  • Speech Enhancement
  • Deep Learning
  • Signal Processing


  • PhD in Electrical Engineering, 2016

    KU Leuven, Belgium

  • MTech in Communication Engineering, 2012

    IIT Bombay, India

  • BTech in Electronics and Communication Engineering, 2009

    College of Engineering Trivandrum, India



Applied Scientist


Oct 2020 – Present Aachen, Germany
Applied Scientist in the Alexa Speech team.

Post-doctoral Researcher

Idiap Research Institute

May 2019 – Jun 2020 Martigny, Switzerland
Worked with Prof. Herve Bourlard on sparse and hierarchical representations for speech modeling and automatic speech recognition.

Post-doctoral Researcher

Ghent University, Belgium

Feb 2017 – Apr 2019 Ghent, Belgium

Working on the ERC project RobSpear developing deep learning aided strategies for correcting non-linear hearing deficits. Responsibilities include:

  • Designing experiments
  • Developing deep learning-based architectures
  • Supervising PhD students and masters thesis

Visiting Researcher

Nuance Communications, Inc.

Apr 2015 – Jun 2015 Merelbeke, Belgium
Investigated the previously proposed exemplar-based speech enhancement approaches as front-end for Nuance’s ASR tasks on automotive data.

Visiting Researcher

Tampere Univsersity of Technology

Jun 2013 – Aug 2013 Tampere, Finland
Investigated the use of Modulation Envelope features for feature enhancement to improve the noise robustness of Automatic Speech Recognition systems with Audio Research Group, Dept. of Signal Processing, TUT. Proposed an approach using coupled dictionaries for exemplar-based speech/feature enhancement.

Academic Service


2018 – 2019 : Arthur Van Den Broucke, MS Student, Ghent University, Belgium
Masters Thesis : Deep learning techniques for individualized auditory profiles

2014 – 2015 : Joris Verhaegen, MS Student, KU Leuven, Belgium
Masters Thesis: NMF-based reduction of background sounds in TV shows for better ASR

Summer 2015 : Shubham Sharma, Summer Intern, KU Leuven, Belgium
Topic: De-reverberation using non-negative matrix factorisation

Summer 2012 : Rainu V. Philip, Anjaly Elias, Summer Interns, IIT Bombay, India
Topic: Face detection using sparse representations based on compressive sensing algorithms



  • IEEE Transactions on Audio, Speech and Language Processing
  • IEEE Transactions on Signal Processing
  • IEEE Signal Processing Letters
  • Computer, Speech & Language
  • Speech Communication
  • Journal of Acoustical Society of America
  • Neural Networks (Elsevier)


  • IEEE International Conference on Multimedia & Expo (ICME)



A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications

NICE Keras

Implementation of NICE: Nonlinear Independent Components Estimation in Keras


Tricks to improve SEGAN performance. Eveything is re-implemented into Keras with Tensorflow backend.

SERGAN: Speech enhancement relativistic generative adversarial network

A fully convolutional end-to-end speech enhancement system with GANs

Joint dereverberation and denoising using NMD

MATLAB impelementation of a joint dereverberation and denoising algorithm based on non-negative matrix deconvolution


Kaldi script for CNN-DNN evaluation on CHiME-3 data