Robust ASR front-end using spectral-based and discriminant features: experiments on Aurora tasks

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F01%3APU23547" target="_blank" >RIV/00216305:26220/01:PU23547 - isvavai.cz</a>
Result on the web
—
DOI - Digital Object Identifier
—

Result language
angličtina
Original language name
Robust ASR front-end using spectral-based and discriminant features: experiments on Aurora tasks
Original language description
This paper describes an automatic speech recognition front-end that combines low-level robust ASR feature extraction tech-niques, and higher-level linear and non-linear feature transformations. The low-level algorithms use data-derived filters, mean andvariance normalization of the feature vectors, and dropping of noise frames. The feature vectors are then linearly transformed using Principal Components Analysis (PCA). An Artificial Neural Network (ANN) is also used to compute features that are usefulfor classification of speech sounds. It is trained for phoneme probability estimation on a large corpus of noisy speech. These transformations lead to two feature streams whose vectors are concatenated and then used for speech recognition. This method was tested on the set of speech corpora used for the "Aurora" evaluation. Using the feature stream generated without the ANN yields an overall 41% reduction of the error rate over Mel-Frequency Cepstral Coefficients (MFCC) reference feature
Czech name
—
Czech description
—

Publication year
2001
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Similar results(10)