'Robust speech recognition' diaporamas de présentation

Enhanced Speech Models for Robust Speech Recognition

Enhanced Speech Models for Robust Speech Recognition. Juan Arturo Nolazco-Flores Dpto. de Ciencias Computacinales ITESM, campus Monterrey. Talk Overview. Introduction Enhanced-Speech Models Coments and Conclusions. Questions?. Introduction. Problem:

632 views • 47 slides

Grid Computing in Hong Kong

516 views • 39 slides

CHiME Challenge:

605 views • 32 slides

CHiME Challenge:

328 views • 32 slides

Introduction

Introduction. The PDF of cepstral coefficients of speech signals Is usually regarded as a quasi-Gaussian distribution Under this assumption, the purpose of moment normalization of order N is then to have For odd order moments : For even order moments : CMS is to normalize the first moment

392 views • 31 slides

Neuro-IT Roadmap: Successful in the Physical World

Neuro-IT Roadmap: Successful in the Physical World. Robust perception Image processing Speech recognition Multimodal human machine interaction System integration Scene analysis and representation. Automotive: Overtake-Checker and Door-Opener Assistant. Dr. Axel Techmer

387 views • 27 slides

Overview:

PROPAGATION OF STATISTICAL INFORMATION THROUGH NON-LINEAR FEATURE EXTRACTIONS FOR ROBUST SPEECH RECOGNITION. Overview:. Introduction: Automatic speech recognition. Problem: Imperfect noise suppression. Proposed solution: Uncertainty propagation. Tests & results. Conclusions.

320 views • 22 slides

Compensating speaker-to-microphone playback system for robust speech recognition

Compensating speaker-to-microphone playback system for robust speech recognition. So-Young Jeong and Soo-Young Lee Brain Science Research Center and Department of Electrical Engineering and Computer Science Korea Advanced Institute of Science and Technology. Distorted speech. Clean speech.

227 views • 10 slides

Automatic Speech Recognition - Edukite

The purpose of the Automatic Speech Recognition course is to introduce you to the rapidly growing field automatic speech recognition. The course is sectioned into three parts. The first part teaches the background material in the acoustic theory of speech production, acoustic-phonetics, and signal representation while the second part deals with the algorithmic aspects of speech recognition systems including pattern classification, search algorithms, stochastic modelling, and language modelling techniques. See More: https://bit.ly/2HksJws

112 views • 9 slides