Multi‐style training for robust speech recognition under stress

Robust Speech Recognition Training

Improving the performance of MFCC for Persian robust speech recognition

The Mel Frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. In this paper to achieve a satisfactorily performance in Automatic Speech Recognition (ASR) applications we introduce a noise robust new set of MFCC vector estimated through following steps. First, spectral mean normalization is a pre-processing which applies to t...

Evaluation of Acoustic Correlates of Speech Under Stress for Robust Speech Recognition

This paper presents results from an investigation of how speech characteristics change under varying levels of stress with specific application to improving automatic isolated-word speech recognition. In JASA-87 [2], preliminary results were presented, based on a series of probe studies which served to identify possible stress relayers in a speech recognition/communication framework. This paper...

Speech Recognition under Stress Condition

The objective of this work is to conduct a speech recognition study and evaluate the performance of the same under stressed condition. The speech recognition study is conducted both in isolated word recognition and keyword spotting approaches. The word models are built during training using speech collected from neutral condition. During testing these models are tested with speech signals colle...

improving the performance of mfcc for persian robust speech recognition

the mel frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. in this paper to achieve a satisfactorily performance in automatic speech recognition (asr) applications we introduce a noise robust new set of mfcc vector estimated through following steps. first, spectral mean normalization is a pre-processing which applies to t...