Search Results for author: Sanjeev Satheesh

Found 11 papers, 3 papers with code

Topic Compositional Neural Language Model

no code implementations28 Dec 2017 Wenlin Wang, Zhe Gan, Wenqi Wang, Dinghan Shen, Jiaji Huang, Wei Ping, Sanjeev Satheesh, Lawrence Carin

The TCNLM learns the global semantic coherence of a document via a neural topic model, and the probability of each learned latent topic is further used to build a Mixture-of-Experts (MoE) language model, where each expert (corresponding to one topic) is a recurrent neural network (RNN) that accounts for learning the local structure of a word sequence.

Language Modelling

Robust Speech Recognition Using Generative Adversarial Networks

no code implementations5 Nov 2017 Anuroop Sriram, Heewoo Jun, Yashesh Gaur, Sanjeev Satheesh

This paper describes a general, scalable, end-to-end framework that uses the generative adversarial network (GAN) objective to enable robust speech recognition.

Robust Speech Recognition speech-recognition

Cold Fusion: Training Seq2Seq Models Together with Language Models

no code implementations ICLR 2018 Anuroop Sriram, Heewoo Jun, Sanjeev Satheesh, Adam Coates

Sequence-to-sequence (Seq2Seq) models with attention have excelled at tasks which involve generating natural language sentences such as machine translation, image captioning and speech recognition.

Image Captioning Language Modelling +4

Exploring Neural Transducers for End-to-End Speech Recognition

no code implementations24 Jul 2017 Eric Battenberg, Jitong Chen, Rewon Child, Adam Coates, Yashesh Gaur, Yi Li, Hairong Liu, Sanjeev Satheesh, David Seetapun, Anuroop Sriram, Zhenyao Zhu

In this work, we perform an empirical comparison among the CTC, RNN-Transducer, and attention-based Seq2Seq models for end-to-end speech recognition.

Language Modelling speech-recognition +1

Reducing Bias in Production Speech Models

no code implementations11 May 2017 Eric Battenberg, Rewon Child, Adam Coates, Christopher Fougner, Yashesh Gaur, Jiaji Huang, Heewoo Jun, Ajay Kannan, Markus Kliegl, Atul Kumar, Hairong Liu, Vinay Rao, Sanjeev Satheesh, David Seetapun, Anuroop Sriram, Zhenyao Zhu

Replacing hand-engineered pipelines with end-to-end deep learning systems has enabled strong results in applications like speech and object recognition.

Object Recognition

Gram-CTC: Automatic Unit Selection and Target Decomposition for Sequence Labelling

no code implementations ICML 2017 Hairong Liu, Zhenyao Zhu, Xiangang Li, Sanjeev Satheesh

These methods suffer from two major drawbacks: 1) the set of basic units is fixed, such as the set of words, characters or phonemes in speech recognition, and 2) the decomposition of target sequences is fixed.

speech-recognition Speech Recognition

Active Learning for Speech Recognition: the Power of Gradients

no code implementations10 Dec 2016 Jiaji Huang, Rewon Child, Vinay Rao, Hairong Liu, Sanjeev Satheesh, Adam Coates

For speech recognition, confidence scores and other likelihood-based active learning methods have been shown to be effective.

Active Learning Informativeness +2

ImageNet Large Scale Visual Recognition Challenge

12 code implementations1 Sep 2014 Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, Li Fei-Fei

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classification and detection on hundreds of object categories and millions of images.

General Classification Image Classification +3

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