András Zolnay

Followers

Following

Co-authors

Public Views

Michael Spivey

University of California, Merced

Joaquim Llisterri

Universitat Autònoma de Barcelona

Marc Champagne

Kwantlen Polytechnic University

Paul Williams

Indiana University

DAVE COWLEY

Historic Environment Scotland

George Christodoulides

Université de Mons

rudolph bauer

Washington Center for Consciousness Studies

Amir Mosavi

German Research Center for Artificial Intelligence

Munish Jindal

Punjab Technical University

Rym Meddeb

University of Tunis El Manar

Interests

Uploads

Papers by András Zolnay

Using multiple acoustic feature sets for speech recognition

by Ralf Schlüter, András Zolnay, and Kocharov Daniil

Speech Communication, 2007

In this paper, the use of multiple acoustic feature sets for speech recognition is investigated. ... more In this paper, the use of multiple acoustic feature sets for speech recognition is investigated. The combination of both auditory as well as articulatory motivated features is considered. In addition to a voicing feature, we introduce a recently developed articulatory motivated feature, the spectrum derivative feature. Features are combined both directly using linear discriminant analysis (LDA) as well as indirectly on model level using discriminative model combination (DMC). Experimental results are presented for both small-and large-vocabulary tasks. The results show that the accuracy of automatic speech recognition systems can be significantly improved by the combination of auditory and articulatory motivated features. The word error rate is reduced from 1.8% to 1.5% on the SieTill task for German digit string recognition. Consistent improvements in word error rate have been obtained on two large-vocabulary corpora. The word error rate is reduced from 19.1% to 18.4% on the VerbMobil II corpus, a German large-vocabulary conversational speech task, and from 14.1% to 13.5% on the British English part of the European parliament plenary sessions (EPPS) task from the 2005 TC-STAR ASR evaluation campaign.

Download

Feature combination using linear discriminant analysis and its pitfalls

by Ralf Schlüter and András Zolnay

In this paper, Linear Discriminant Analysis (LDA) is investigated with respect to the combination... more In this paper, Linear Discriminant Analysis (LDA) is investigated with respect to the combination of different acoustic features for automatic speech recognition. It is shown that the combination of acoustic features using LDA does not consistently lead to improve- ments in word error rate. A detailed analysis of the recognition results on the Verbmobil (VM II) and on the English portion of the European Parliament Plenary Sessions (EPPS) corpus is given. This includes an independent analysis of the effect of the dimen- sion of the input to LDA, the effect of strongly correlated input features, as well as a detailed numerical analysis of the generalized eigenvalue problem underlying LDA. Relative improvements in word error rate of up to 5% were observed for LDA-based com- bination of multiple acoustic features.

Download

Acoustic Feature Combination for Robust Speech Recognition

by Ralf Schlüter and András Zolnay

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2005

In this paper, we consider the use of multiple acoustic features of the speech signal for robust ... more In this paper, we consider the use of multiple acoustic features of the speech signal for robust speech recognition. We investigate the combination of various auditory based (Mel Frequency Cepstrum Coefficients, Perceptual Linear Prediction, etc.) and articulatory based (voicedness) features. Features are combined by a Linear Discriminant Analysis based and by a log-linear model combination based techniques. We describe the two feature combination techniques and compare the experimental results. Experiments performed on the large-vocabulary task VerbMobil II (German conversational speech) show that the accuracy of automatic speech recognition systems can be improved by the combination of different acoustic features.

Download

Extraction methods of voicing feature for robust speech recognition

by Ralf Schlüter and András Zolnay

In this paper, three different voicing features are studied as additional acoustic features for c... more In this paper, three different voicing features are studied as additional acoustic features for continuous speech recognition. The harmonic product spectrum based feature is extracted in frequency domain while the autocorrelation and the average magnitude difference based methods work in time domain. The algorithms produce a measure of voicing for each time frame. The voicing measure was combined with the standard Mel Frequency Cepstral Coefficients (MFCC) using linear discriminant analysis to choose the most relevant features. Experiments have been performed on small and large vocabulary tasks. The three different voicing measures combined with MFCCs resulted in similar improvements in word error rate: improvements of up to 14% on the smallvocabulary task and improvements of up to 6% on the largevocabulary task relative to using MFCC alone with the same overall number of parameters in the system.

Download

Accurate CT∕MR vessel-guided nonrigid registration of largely deformed livers

Medical Physics, 2012

Computer tomography (CT) scans are used for designing radiotherapy treatment plans. However, the ... more Computer tomography (CT) scans are used for designing radiotherapy treatment plans. However, the tumor is often better visible in magnetic resonance (MR) images. For liver stereotactic body radiation therapy (SBRT), the planning CT scan is acquired while abdominal compression is applied to reduce tumor motion induced by breathing. However, diagnostic MR scans are acquired under voluntary breath-hold without the compression device. The resulting large differences in liver shape hinder the alignment of CT and MR image sets, which severely limits the integration of the information provided by these images. The purpose of the current study is to develop and validate a nonrigid registration method to align breath-hold MR images with abdominal-compressed CT images, using vessels that are automatically segmented within the liver. Contrast-enhanced MR and CT images of seven patients with liver cancer were used for this study. The registration method combines automatic vessel segmentation with an adapted version of thin-plate spline robust point matching. The vessel segmentation uses a multiscale vesselness measure, which allows vessels of various thicknesses to be segmented. The nonrigid registration is point-based, and progressively improves the correspondence and transformation between two point sets. Moreover, the nonrigid registration is capable of identifying and handling outliers (points with no counterpart in the other set). We took advantage of the strengths of both methods and created a multiscale registration algorithm. First, thick vessels are registered, then with each new iteration thinner vessels are included in the registration (strategy A). We compared strategy A to a straightforward approach where vessels of various diameters are segmented and subsequently registered (strategy B). To assess the transformation accuracy, residual distances were calculated for vessel bifurcations. For anatomical validation, residual distances were calculated for additional anatomical landmarks within the liver. To estimate the extent of deformation, the residual distances for the aforementioned anatomical points were calculated after rigid registration. Liver deformations in the range of 2.8-10.7 mm were found after rigid registration of the CT and MR scans. Low residual distances for vessel bifurcations (average 1.6, range 1.3-1.9 mm) and additional anatomical landmarks (1.5, 1.1-2.4 mm) were found after nonrigid registration. A large amount of outliers were identified (25%-55%) caused by vessels present in only one of the image sets and false positives in the vesselness measure. The nonrigid registration was capable of handling these outliers as was demonstrated by the low residual distances. Both strategies yielded very similar results in registration accuracy, but strategy A was faster than strategy B (≥2.0 times). An accurate CT∕MR vessel-guided nonrigid registration for largely deformed livers was developed, tested, and validated. The method, combining vessel segmentation and point matching, was robust against differences in the segmented vessels. The authors conclude that nonrigid registration is required for accurate alignment of abdominal-compressed and uncompressed liver anatomy. Alignment of breath-hold MR and abdominal-compressed CT images can be used to improve tumor localization for liver SBRT.

Articulatory motivated acoustic features for speech recognition

by Ralf Schlüter and András Zolnay

In this paper, we consider the use of multiple acoustic features of the speech signal for continu... more In this paper, we consider the use of multiple acoustic features of the speech signal for continuous speech recognition. A novel articulatory motivated acoustic feature is introduced, namely the spectrum derivative feature. The new feature is tested in combination with the standard Mel Frequency Cepstral Coefficients (MFCC) and the voicedness features. Linear Discriminant Analysis is applied to find the optimal combination of different acoustic features. Experiments have been performed on small and large vocabulary tasks. Significant improvements in word error rate have been obtained by combining the MFCC feature with the articulatory motivated voicedness and spectrum derivative features: improvements of up to 25% on the small-vocabulary task and improvements of up to 4% on the large-vocabulary task relative to using MFCC alone with the same overall number of parameters in the system.

Download

Using multiple acoustic feature sets for speech recognition

by Ralf Schlüter, András Zolnay, and Kocharov Daniil

Speech Communication, 2007

Download

Feature combination using linear discriminant analysis and its pitfalls

by Ralf Schlüter and András Zolnay

Download

Acoustic Feature Combination for Robust Speech Recognition

by Ralf Schlüter and András Zolnay

Proceedings. (ICASSP '05). IEEE International Conference on Acoustics, Speech, and Signal Processing, 2005., 2005

Download