Publication

Speech Processing & Text-Independent Automatic Person Verification

2002
Reports

Abstract

In this communication we first review the human speech production process and feature extraction approaches commonly used in a speaker verification system. Experiments on the telephone speech {NTIMIT} database suggest that the performance degradation of a speaker verification system used in noisy conditions can be reduced by extending traditional feature vectors with recently proposed {MACV} features.

Official source

Graph Chat

Chat with Graph Search

Ask any question about EPFL courses, lectures, exercises, research, news, etc. or try the example questions below.

DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

2002
Reports

Abstract

Official source

Related concepts (21)

Speech production

Speech production is the process by which thoughts are translated into speech. This includes the selection of words, the organization of relevant grammatical forms, and then the articulation of the resulting sounds by the motor system using the vocal apparatus. Speech production can be spontaneous such as when a person creates the words of a conversation, reactive such as when they name a picture or read aloud a written word, or imitative, such as in speech repetition.

Feature selection

Feature selection is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. Stylometry and DNA microarray analysis are two cases where feature selection is used. It should be distinguished from feature extraction. Feature selection techniques are used for several reasons: simplification of models to make them easier to interpret by researchers/users, shorter training times, to avoid the curse of dimensionality, improve data's compatibility with a learning model class, encode inherent symmetries present in the input space.

Feature (machine learning)

In machine learning and pattern recognition, a feature is an individual measurable property or characteristic of a phenomenon. Choosing informative, discriminating and independent features is a crucial element of effective algorithms in pattern recognition, classification and regression. Features are usually numeric, but structural features such as strings and graphs are used in syntactic pattern recognition. The concept of "feature" is related to that of explanatory variable used in statistical techniques such as linear regression.

Related publications (5)

Speech Processing & Text-Independent Automatic Person Verification

Chat with Graph Search

Unsupervised Rhythm and Voice Conversion to Improve ASR on Dysarthric Speech

Filter Bank Design based on Minimization of Individual Aliasing Terms for Minimum Mutual Information Subband Adaptive Beamforming

Timbre and Rhythmic TRAP-TANDEM features for music information retrieval