Speech recognition is very difficult in the context of noisy and corrupted speech. Most conventional techniques need huge databases to estimate speech (or noise) density probabilities to perform recognition. We discuss the potential of perceptive speech analysis and processing in combination with biologically plausible neural network processors. We illustrate the potential of such non-linear processing of speech by means of a preliminary test with recognition of French spoken digits from a small speech database
We propose a Multigranular Automatic Speech Recognizer. The hypothesis is that speech signal contai...
This dissertation presents new modular and integrative information methods and systems inspired by t...
Biological spiking neural networks (SNNs) can temporally encode information in their outputs, e.g. ...
Speech recognition is very difficult in the context of noisy and corrupted speech. Most conventional...
<p>The process of how auditory neurons encode and learn to recognise speech signals is still an open...
<p>The process of how auditory neurons encode and learn to recognise speech signals is still an open...
Speech recognition has become an important task to improve the human-machine interface. Taking into...
Speech recognition is one of the most important problems in artificial intelligence today. Despite n...
Automatic speech recognition accuracy is affected adversely by the presence of noise. In this paper ...
The field of digital speech processing may be divided into three distinct and somewhat in- depend...
This paper presents a speech recognition system which incorporates predictive neural networks. The ...
The auditory neural code is resilient to acoustic variability and capable of recognizing sounds amon...
Specific locations within the brain contain neurons which respond, by firing action potentials (spik...
Recurrent spiking neurons with lateral inhibition connection play a vital role in human’s brain func...
Shortcomings of automatic speech recognition (ASR) applications are becoming more evident as they ar...
We propose a Multigranular Automatic Speech Recognizer. The hypothesis is that speech signal contai...
This dissertation presents new modular and integrative information methods and systems inspired by t...
Biological spiking neural networks (SNNs) can temporally encode information in their outputs, e.g. ...
Speech recognition is very difficult in the context of noisy and corrupted speech. Most conventional...
<p>The process of how auditory neurons encode and learn to recognise speech signals is still an open...
<p>The process of how auditory neurons encode and learn to recognise speech signals is still an open...
Speech recognition has become an important task to improve the human-machine interface. Taking into...
Speech recognition is one of the most important problems in artificial intelligence today. Despite n...
Automatic speech recognition accuracy is affected adversely by the presence of noise. In this paper ...
The field of digital speech processing may be divided into three distinct and somewhat in- depend...
This paper presents a speech recognition system which incorporates predictive neural networks. The ...
The auditory neural code is resilient to acoustic variability and capable of recognizing sounds amon...
Specific locations within the brain contain neurons which respond, by firing action potentials (spik...
Recurrent spiking neurons with lateral inhibition connection play a vital role in human’s brain func...
Shortcomings of automatic speech recognition (ASR) applications are becoming more evident as they ar...
We propose a Multigranular Automatic Speech Recognizer. The hypothesis is that speech signal contai...
This dissertation presents new modular and integrative information methods and systems inspired by t...
Biological spiking neural networks (SNNs) can temporally encode information in their outputs, e.g. ...