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With the explosion of protein sequences generated in the postgenomic era, there is a need for the development of computational methods to characterize and classify them as an alternative to the experimental methods that are expensive and time consuming. Although the amino acid chains that constitute proteins are originally symbolic chains they can be converted into numerical sequences and processed...
Automatic speech recognition (ASR) in noisy environments remains a challenging goal. Recently, the idea of estimating the uncertainty about the features obtained after speech enhancement and propagating it to dynamically adapt deep neural network (DNN) based acoustic models has raised some interest. However, the results in the literature were reported on simulated noisy datasets for a limited variety...
We consider the problem of robust automatic speech recognition (ASR) in the context of the CHiME-3 Challenge. The proposed system combines three contributions. First, we propose a deep neural network (DNN) based multichannel speech enhancement technique, where the speech and noise spectra are estimated using a DNN based regressor and the spatial parameters are derived in an expectation-maximization...
For automatic speech recognition (ASR) systems it is important that the input signal mainly contains the desired speech signal. For a compact arrangement, differential microphone arrays (DMAs) are a suitable choice as front-end of ASR systems. The limiting factor of DMAs is the white noise gain, which can be treated by the minimum norm solution (MNS). In this paper, we introduce the first time the...
This paper addresses the problem of distant speech recognition in reverberant noisy conditions employing a microphone array. We present a prototype system that can segment the utterances in real-time and generate robust ASR results off-line. The segmentation is carried out by a voice activity detector based on deep belief networks, the speaker localization by a position-pitch plane, and the enhancement...
Recently, we developed pre-image iteration methods for single-channel speech enhancement. We used objective quality measures for evaluation. In this paper, we evaluate the de-noising capabilities of pre-image iterations using an automatic speech recognizer trained on clean speech data. In particular, we provide the word recognition accuracy of the de-noised utterances using white and car noise at...
The electro-larynx device (EL) offers the possibility to re-obtain speech when the larynx is removed after a total laryngectomy. Speech produced with an EL suffers from inadequate speech sound quality, therefore there is a strong need to enhance EL speech. When disordered speech is applied to Automatic Speech Recognition (ASR) systems, the performance will significantly decrease. ASR systems are increasingly...
This paper deals with the problem of searching for a suitable window for robust speech recognition in noisy conditions. A set of asymmetric windows, so-called DDRc,w, are proposed which are controlled by two parameters, center c and width w. These windows are derived from the DDR window used in the higher-lag autocorrelation spectrum estimation (HASE) method and act over the OSA (One-Sided Autocorrelation)...
This paper presents a noise estimation technique based on knowledge of pitch information for robust speech recognition. In the first stage the noise is estimated by means of extrapolating the noise from frames where speech is believed to be absent. These frames are detected with a proposed pitch based VAD (Voice Activity Detector). In the second stage the noise estimation is revised in voiced frames...
This paper presents a feature compensation technique based on the minimum mean square error (MMSE) estimation for robust speech recognition. Similarly to other MMSE compensation methods based on stereo data, our approach models the differences between clean and noisy feature spaces, and the resulting MMSE estimate of the clean feature vector is obtained as a piece-wise linear transformation of the...
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