Robustness in Automatic Speech Recognition: Fundamentals and Applications
Robustness in Automatic Speech Recognition: Fundamentals and Applications
Speech enhancement using voice source models
Speech enhancement using voice source models
A computationally efficient mel-filter bank VAD algorithm for distributed speech recognition systems
EURASIP Journal on Applied Signal Processing
Energy-based VAD with grey magnitude spectral subtraction
Speech Communication
Noise robust voice activity detection based on periodic to aperiodic component ratio
Speech Communication
Comparison of the impact of some Minkowski metrics on VQ/GMM based speaker recognition
Computers and Electrical Engineering
Combining pulse-based features for rejecting far-field speech in a HMM-based Voice Activity Detector
Computers and Electrical Engineering
Trial pruning based on genetic algorithm for single-trial EEG classification
Computers and Electrical Engineering
Robust Voice Activity Detection Using Long-Term Signal Variability
IEEE Transactions on Audio, Speech, and Language Processing
A study of voice activity detection techniques for NIST speaker recognition evaluations
Computer Speech and Language
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This paper introduces a nonlinear function into the frequency spectrum that improves the detection of vowels, diphthongs, and semivowels within the speech signal. The lower efficiency of consonant detection was solved by implementing the hangover and hangbefore criteria. This paper presents a procedure for faster definition of those optimal constants used by hangover and hangbefore criteria. A nonlinearly changed frequency spectrum is used in the proposed GMM (Gaussian Mixture Model) based VAD (Voice Activity Detection) algorithm. Comparative tests between the proposed VAD algorithm and seven other VAD algorithms were made on the Aurora 2 database. The experiments were based on frame error detection and on speech recognition performance for two types of acoustic training modes (multi-condition and clean only). The lowest average percentage of frame errors was obtained by the proposed VAD algorithm, which also achieved positive improvement in the speech recognition performance for both types of acoustic training modes.