Automatic speech recognition, especially large vocabulary continuous speech recognition,
is an important issue in the field of machine learning. For a long time, the hidden Markov …

We present a structured overview of adaptation algorithms for neural network-based speech
recognition, considering both hybrid hidden Markov model/neural network systems and end …

In the last decade of automatic speech recognition (ASR) research, the introduction of deep
learning has brought considerable reductions in word error rate of more than 50% relative …

Many state-of-the-art Large Vocabulary Continuous Speech Recognition (LVCSR) Systems
are hybrids of neural networks and Hidden Markov Models (HMMs). Recently, more direct …

We present an approach to training neural networks to generate sequences using actor-
critic methods from reinforcement learning (RL). Current log-likelihood training methods are …

We present a library of efficient implementations of deep learning primitives. Deep learning
workloads are computationally intensive, and optimizing their kernels is difficult and time …

The performance of automatic speech recognition (ASR) has improved tremendously due to
the application of deep neural networks (DNNs). Despite this progress, building a new ASR …

In this paper, we present TED-LIUM release 3 corpus (TED-LIUM 3 is available on
https://lium. univ-lemans. fr/ted-lium3/) dedicated to speech recognition in English, which …

Encoder-decoder based sequence-to-sequence models have demonstrated state-of-the-art
results in end-to-end automatic speech recognition (ASR). Recently, the transformer …

In this paper, we propose novel stochastic modeling of various components of a continuous
sign language recognition (CSLR) system that is based on the transformer encoder and …