This paper investigates whether modulation domain speech en-hancement methods are better than corresponding acoustic do-main methods when used as a preprocessor to automatic speech recognition. It is well known that linguistic information of speech is contained not only in the short-time magnitude spec-trum but also in its temporal evolution. In addition, this study investigates whether popular metrics used in speech enhance-ment (such as PESQ, segmental SNR, STOI) are indicative of ASR performance. ASR experiments on the TIMIT speech cor-pus corrupted by various noises were performed to compare re-cent modulation domain methods with their acoustic domain variants.Full Tex
This report presents a review of the main research directions in noise robust automatic speech recog...
Modulation domain has been reported to be a better alternative to Frequency domain for speech enhanc...
This paper evaluates speech enhancement by filtering in the modulation frequency domain, as an alte...
The goal of a speech enhancement algorithm is to reduce or eliminate background noise without distor...
In this paper, we analyze the temporal modulation char-acteristics of speech and noise from a speech...
This thesis explores some of the main approaches to the problem of speech signal enhancement. Tradi...
In this paper we investigate the modulation domain as an alter-native to the acoustic domain for spe...
Automatic speech recognition in everyday environments must be robust to significant levels of reverb...
The paper describes a system for automatic speech recognition (ASR) that is benchmarked with data of...
[[abstract]]In this paper, we present two novel algorithms to improve the noise robustness of featur...
[[abstract]]In this paper, we present two novel algorithms to improve the noise robustness of featur...
[[abstract]]In this paper, we present two novel algorithms to improve the noise robustness of featur...
[[abstract]]In this paper, we present two novel algorithms to improve the noise robustness of featur...
[[abstract]]In this paper, we present two novel algorithms to improve the noise robustness of featur...
Studies have shown that the performance of state-of-the-art automatic speech recognition (ASR) syste...
This report presents a review of the main research directions in noise robust automatic speech recog...
Modulation domain has been reported to be a better alternative to Frequency domain for speech enhanc...
This paper evaluates speech enhancement by filtering in the modulation frequency domain, as an alte...
The goal of a speech enhancement algorithm is to reduce or eliminate background noise without distor...
In this paper, we analyze the temporal modulation char-acteristics of speech and noise from a speech...
This thesis explores some of the main approaches to the problem of speech signal enhancement. Tradi...
In this paper we investigate the modulation domain as an alter-native to the acoustic domain for spe...
Automatic speech recognition in everyday environments must be robust to significant levels of reverb...
The paper describes a system for automatic speech recognition (ASR) that is benchmarked with data of...
[[abstract]]In this paper, we present two novel algorithms to improve the noise robustness of featur...
[[abstract]]In this paper, we present two novel algorithms to improve the noise robustness of featur...
[[abstract]]In this paper, we present two novel algorithms to improve the noise robustness of featur...
[[abstract]]In this paper, we present two novel algorithms to improve the noise robustness of featur...
[[abstract]]In this paper, we present two novel algorithms to improve the noise robustness of featur...
Studies have shown that the performance of state-of-the-art automatic speech recognition (ASR) syste...
This report presents a review of the main research directions in noise robust automatic speech recog...
Modulation domain has been reported to be a better alternative to Frequency domain for speech enhanc...
This paper evaluates speech enhancement by filtering in the modulation frequency domain, as an alte...