Robust Speech Recognition and Understanding

Robust Speech Recognition and Understanding

This book on Robust Speech Recognition and Understanding brings together many different aspects of the current research on automatic speech recognition and language understanding.

Description

The first four chapters address the task of voice activity detection which is considered an important issue for all speech recognition systems. The next chapters give several extensions to state-of-the-art HMM methods. Furthermore, a number of chapters particularly address the task of robust ASR under noisy conditions. Two chapters on the automatic recognition of a speaker’s emotional state highlight the importance of natural speech understanding and interpretation in voice-driven systems. The last chapters of the book address the application of conversational systems on robots, as well as the autonomous acquisition of vocalization skills.

Table of Contents

  • Voice Activity Detection. Fundamentals and Speech Recognition System Robustness
  • Bimodal Emotion Recognition using Speech and Physiological Changes
  • Emotion Estimation in Speech Using a 3D Emotion Space Concept
  • Linearly Interpolated Hierarchical N-gram Language Models for Speech Recognition Engines
  • A Factored Language Model for Prosody Dependent Speech Recognition
  • Early Decision Making in Continuous Speech
  • Analysis and Implementation of an Automated Delimiter of “Quranic” Verses in Audio Files using Speech Recognition Techniques
  • An Improved GA Based Modified Dynamic Neural Network for Cantonese-Digit Speech Recognition
  • Talking Robot and the Autonomous Acquisition of Vocalization and Singing Skill
  • Conversation System of an Everyday Robot Robovie-IV
  • Sound Localization of Elevation using Pinnae for Auditory Robots
  • Autocorrelation-based Methods for Noise-Robust Speech Recognition
  • The Research of Noise-Robust Speech Recognition Based on Frequency Warping Wavelet
  • Uncertainty in Signal Estimation and Stochastic Weighted Viterbi Algorithm: A Unified Framework to Address Robustness in Speech Recognition and Speaker Verification
  • Novel Approaches to Speech Detection in the Processing of Continuous Audio Streams
  • New Advances in Voice Activity Detection using HOS and Optimization Strategies
  • Voice and Noise Detection with AdaBoost
  • Evolutionary Speech Recognition
  • Using Genetic Algorithm to Improve the Performance of Speech Recognition Based on Artificial Neural Network
  • A General Approximation-Optimization Approach to Large Margin Estimation of HMMs
  • Double Layer Architectures for Automatic Speech Recognition Using HMM
  • Audio Visual Speech Recognition and Segmentation Based on DBN Models
  • Discrete-Mixture HMMs-based Approach for Noisy Speech Recognition
  • Speech Recognition in Unknown Noisy Conditions
  • Speech Recognition Under Noise Conditions: Compensation Methods

Book Details

Author(s): Michael Grimm and Kristian Kroschel.
Publisher: I-Tech Education and Publishing
Format(s): PDF, HTML
File size: 5.60 MB
Number of pages: 460
Link: Download.








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