This paper outlines structures of different automatic speech recognition systems, hybrid and end-to-end, pros and cons for each of them, including the comparison of training data and computational resources requirements. Three main approaches to speech recognition are considered: hybrid Hidden Markov Model – Deep Neural Network, end-to-end Connectionist Temporal Classification and Sequence-to-Sequence. The Listen, Attend, and Spell approach is chosen as an example for the Sequence-to-Sequence model
The general subject of this work is to present mathematical methods encountered in auto-matic speech...
The performance of automatic speech recognition (ASR) system can be enhanced by adaptation of the AS...
This report focuses on a hybrid approach, including stochastic and connectionist methods, for contin...
Although Automatic Speech Recognition (ASR) systems based on hidden Markov models (HMMs) are popular...
In spite of the advances accomplished throughout the last decades, automatic speech recognition (ASR...
In spite of the advances accomplished throughout the last decades by a number of research teams, Aut...
Automatic Speech Recognition (ASR) is a challenging classification task over sequences of acoustic f...
By the analysis on the principle of speech recognition system, a speech recognition system was desig...
Speech is the most efficient way to train a machine or communicate with a machine. This work focuses...
Speech recognition is the application of sophisticated algorithms which involve the transforming of ...
Standard automatic speech recognition (ASR) systems follow a divide and conquer approach to convert ...
The main goal in this research is to find out possible ways to built hybrid systems, based on neural...
AbstractThis paper introduces a novel insight to the problem of Automatic Speech Recognition (ASR). ...
Over these last few years, the use of Artificial Neural Networks (ANNs), now often referred to as de...
Today, automatic speech recognition (ASR) is widely used for different purposes such as robotics, mu...
The general subject of this work is to present mathematical methods encountered in auto-matic speech...
The performance of automatic speech recognition (ASR) system can be enhanced by adaptation of the AS...
This report focuses on a hybrid approach, including stochastic and connectionist methods, for contin...
Although Automatic Speech Recognition (ASR) systems based on hidden Markov models (HMMs) are popular...
In spite of the advances accomplished throughout the last decades, automatic speech recognition (ASR...
In spite of the advances accomplished throughout the last decades by a number of research teams, Aut...
Automatic Speech Recognition (ASR) is a challenging classification task over sequences of acoustic f...
By the analysis on the principle of speech recognition system, a speech recognition system was desig...
Speech is the most efficient way to train a machine or communicate with a machine. This work focuses...
Speech recognition is the application of sophisticated algorithms which involve the transforming of ...
Standard automatic speech recognition (ASR) systems follow a divide and conquer approach to convert ...
The main goal in this research is to find out possible ways to built hybrid systems, based on neural...
AbstractThis paper introduces a novel insight to the problem of Automatic Speech Recognition (ASR). ...
Over these last few years, the use of Artificial Neural Networks (ANNs), now often referred to as de...
Today, automatic speech recognition (ASR) is widely used for different purposes such as robotics, mu...
The general subject of this work is to present mathematical methods encountered in auto-matic speech...
The performance of automatic speech recognition (ASR) system can be enhanced by adaptation of the AS...
This report focuses on a hybrid approach, including stochastic and connectionist methods, for contin...