The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
In this paper, we investigate the applicability and effectiveness of advanced feature compensation techniques in devising a robust front-end for Automatic Speech Recognition (ASR). First, the Vector Taylor Series (VTS) equations are altered by bringing in the auditory masking factor. The resultant VTS approximation is used to compensate the parameters of a clean speech model and a Minimum Mean Square...
In this paper, we present model domain adaptation algorithms for noise robust speech recognition. We have proposed a Taylor Series expansion of the psychoacoustic corruption function, which provides for superior noise robustness. The proposed joint adaptation algorithm consists of Psychoacoustic Model Compensation (Psy-Comp) and Model-domain Cepstral Mean Normalization (MCMN). While the Psy-Comp compensates...
In this paper, we address the problem of speech recognition in the presence of additive noise. We investigate the applicability and efficacy of auditory masking in devising a robust front end for noisy features. This is achieved by introducing a masking factor into the Vector Taylor Series (VTS) equations. The resultant first order VTS approximation is used to compensate the parameters of a clean...
This paper addresses the problem of speech recognition in the presence of additive noise. It focuses on Psychoacoustic Model Compensation (Psy-Comp) scheme, which has been shown to be a powerful technique for noise robustness. It has further implemented model domain mean and variance normalization along with Psy-Comp to alleviate channel noise for robust continuous speech recognition in noisy conditions...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.