Add to ORKGThis report presents a detail study on psychoacoustic modeling for feature extraction for robust continuous speech recognition. In an automatic speech recognition (ASR) system, feature extraction is critical to determining the recognizer’s performance. The most popular feature vectors for ASR are Mel Frequency Cepstral Coefficients (MFCC). However, it is also well known that its performance drops dramatically under noisy condition. One of the objectives of this research is to improve on the robustness of a continuous speech recognizer.RGM 8/0
In this paper, a feature extraction algorithm for robust speech recognition is introduced. The featu...
State-of-the-art automatic speech recognition (ASR) systems are significantly inferior to humans esp...
Maintaining a high level of robustness for Automatic Speech Recognition (ASR) systems is especially ...
This report presents a detail study on psychoacoustic modeling for feature extraction for robust con...
This thesis presents a detailed study on psychoacoustic modeling for feature extraction for robust s...
The results of investigations into some aspects of robust speech recognition are reported in this th...
A new approach for speech feature extraction in automatic speech recognition (ASR) is proposed in th...
The performance of an automatic speech recognition (ASR) system strongly depends on the representati...
Speech recognition is about what is being said, irrespective of who is saying. Speech recognition is...
Abstract — The Mel-Frequency Cepstral Coefficient (MFCC) or Perceptual Linear Prediction (PLP) featu...
This paper proposes a technique of extracting robust feature vectors for ASR. The technique is inspi...
Abstract — The speech recognition is the most important research area to recognize the speech signal...
Speech recognition is an important and active analysis area of the recent years. This analysis aims ...
Theoretical and practical issues of some of the problems in robust automatic speech recognition (ASR...
The increase in the number of multimedia applications that require robust speech recognition systems...
In this paper, a feature extraction algorithm for robust speech recognition is introduced. The featu...
State-of-the-art automatic speech recognition (ASR) systems are significantly inferior to humans esp...
Maintaining a high level of robustness for Automatic Speech Recognition (ASR) systems is especially ...
This report presents a detail study on psychoacoustic modeling for feature extraction for robust con...
This thesis presents a detailed study on psychoacoustic modeling for feature extraction for robust s...
The results of investigations into some aspects of robust speech recognition are reported in this th...
A new approach for speech feature extraction in automatic speech recognition (ASR) is proposed in th...
The performance of an automatic speech recognition (ASR) system strongly depends on the representati...
Speech recognition is about what is being said, irrespective of who is saying. Speech recognition is...
Abstract — The Mel-Frequency Cepstral Coefficient (MFCC) or Perceptual Linear Prediction (PLP) featu...
This paper proposes a technique of extracting robust feature vectors for ASR. The technique is inspi...
Abstract — The speech recognition is the most important research area to recognize the speech signal...
Speech recognition is an important and active analysis area of the recent years. This analysis aims ...
Theoretical and practical issues of some of the problems in robust automatic speech recognition (ASR...
The increase in the number of multimedia applications that require robust speech recognition systems...
In this paper, a feature extraction algorithm for robust speech recognition is introduced. The featu...
State-of-the-art automatic speech recognition (ASR) systems are significantly inferior to humans esp...
Maintaining a high level of robustness for Automatic Speech Recognition (ASR) systems is especially ...