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Architect - Automatic Speech Recognition

Synctactic - hirist

Location Bangalore, India

Posted 27-December-2021

Description

Role : Principal Architect (Speech Recognition)

This is very senior role in COE and you are going to be key person in Speech Division if selected

Job Description :

12+ years of industry experience and minimum 4-6 years of relevant experience.

Job Location : Bangalore India

Experience : 8 to 18 Years

Desirable Skills :

ML, Knowledge of Speech frameworks such as KALDI EESEN etc, Hands-on experience of current deep learning (DL) techniques like CNN, RNN etc.

Desired experience :

1. Person should have hands-on experience with open source tools such as Kaldi, Pytorch-Kaldi.

2. Hands-on experience of current deep learning (DL) techniques like convolutional neural networks (CNNs), recurrent neural networks (RNNs), long-term short-term memory (LSTM), connectionist temporal classification (CTC), etc used for speech processing is essential.

3. Should be well versed in classical speech processing methodologies like hidden Markov models (HMMs), Gaussian mixture models (GMMs), Artificial neural networks (ANNs), Language modeling, etc.

4. Experience in techniques used for resolving issues related to accuracy, noise, confidence scoring etc.

5. Ability to implement recipes using scripting languages like bash and perl

6. Ability to develop applications using python, c++, Java

7. Familiarity with any of the end-to-end ASR tools such as ESPNET or EESEN or Deep Speech Pytorch is desirable.

8. The candidate should have a good understanding of WFSTs as implemented in OpenFST and Kaldi and should be able to modify the WFST decoder as per the application requirements.

9. Good understanding of machine learning(ML) tools.

Responsibilities :

1. Development of ASR engine using frameworks like DeepSpeech, Kaldi, wav2letter, Pytorch-Kaldi, CMU Sphinx

2. Assist to define technology required for Speech to Text services besides core engine and to design integration of these technologies.

3. Work on improvement of models accuracy and guide the team with best practices.

4. Lead a team of min 2-6 members