When listening in noisy conditions, word recognition seems to be much harder in a non-native language than in one’s native language. Native listeners use both word-initial and word-final information for word recognition in clean listening conditions, where word-initial information is the most important. When listening in noise, however, word-final information becomes relatively more important. ...

The problems of e cacy estimation in noise cancelling by a neural net (LIN-Lateral Inhibition Net [5]) and the use of this information in weighting matching algorithms are focused. Since the e ect of noise on the speech signal is variable and the backpropagation training algorithm is essentially stochastic (most common patterns have more in uence in the weights re-estimation process), it is rea...

In the current ASR systems the presence of competing speakers greatly degrades the recognition performance. This phenomenon is getting even more prominent in the case of hands-free, far-field ASR systems like the “Smart-TV” systems, where reverberation and non-stationary noise pose additional challenges. Furthermore, speakers are, most often, not standing still while speaking. To address these ...

the use of neural networks methodology is not as common in the investigation and pre-diction noise as statistical analysis. the application of artificial neural networks for pre-diction of power tiller noise is set out in the present paper. the sound pressure signals for noise analysis were obtained in a field experiment using a 13-hp power tiller. during measurement and recording of the sound ...

The preparation of training data for statistical parametric speech synthesis can be sophisticated. To ensure the good quality of synthetic speech, high quality low noise recording must be prepared. The preparation of recording script can be also tremendous from words collection, words selection and sentences design. It requires tremendous human effort and takes a lot of time. In this study, we ...

In this paper, the accumulated Kullback divergence (AKD) is used to analyze ASR performance deterioration due to the presence of background noise. The AKD represents a distance between the feature value distribution observed during training and the distribution of the observations in the noisy test condition for each individual feature vector component. In our experiments the AKD summed over al...

In this paper, a novel method is proposed for the recognition of noisy printed characters. The method is based on the representation of the shape of a character by two Hidden Markov Models. Recognition is achieved by scoring these models against the test pattern and combining the results. The method has been evaluated using Baird's noise model, producing a peak performance of 99.5% on the test ...

In this paper, we investigate a closed-loop auditory model and explore its potential as a feature representation for speech recognition. The closed-loop representation consists of an auditory-based, efferent-inspired feedback mechanism that regulates the operating point of a filter bank, thus enabling it to dynamically adapt to changing background noise. With dynamic adaptation, the closed-loop...