A robust training algorithm for adverse speech recognition

A robust training algorithm for adverse speech recognition

In this paper, a new robust training algorithm is proposed for the generation of a set of bias-removed, noise-suppressed reference speech HMM models in adverse environment su€ering from both channel bias and additive noise. Its main idea is to incorporate a signal bias-compensation operation and a PMC noise-compensation operation into its iterative training process. This makes the resulting spe...

A robust environment-effects suppression training algorithm for adverse Mandarin speech recognition

In this paper, a new robust training algorithm for the generation of a set of bias-removed, noise-suppressed reference speech HMM models directly from a training database collected in adverse environment suffering with both convolutional channel bias and additive noise is proposed. Its main idea is to incorporate a signal biascompensation operation and a PMC noise-compensation operation into it...

Robust Speech Recognition Training

Robust Speech Recognition for Adverse Environments

As the state-of-the-art speech recognizers can achieve a very high recognition rate for clean speech, the recognition performance generally degrades drastically under noisy environments. Noise-robust speech recognition has become an important task for speech recognition in adverse environments. Recent research on noise-robust speech recognition mostly focused on two directions: (1) removing the...

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...