نتایج جستجو برای: gmm

تعداد نتایج: 5515  

2013
Zuheng Ming Denis Beautemps Gang Feng

In this paper, we present a statistical method based on GMM modeling to map the acoustic speech spectral features to visual features of Cued Speech in the regression criterion of Minimum Mean-Square Error (MMSE) in a low signal level which is innovative and different with the classic text-to-visual approach. Two different training methods for GMM, namely Expecting-Maximization (EM) approach and...

2013
Matthias Paulik

This paper investigates a method for training bottleneck (BN) features in a more targeted manner for their intended use in GMM-HMM based ASR. Our approach adds a GMM acoustic model activation layer to a standard BN feature extraction (FE) neural network and performs lattice-based MMI training on the resulting network. After training, the network is reverted back into a working BN FE network by ...

2010
Man-Wai Mak Wei Rao

Using GMM-supervectors as the input to SVM classifiers (namely, GMM-SVM) is one of the promising approaches to text-independent speaker verification. However, one unaddressed issue of this approach is the severe imbalance between the numbers of speaker-class utterances and impostor-class utterances available for training a speaker-dependent SVM. This paper proposes a resampling technique – name...

2011
Amr H. Nour-Eldin Peter Kabal

In this paper, we extend our previous work on exploiting speech temporal properties to improve Bandwidth Extension (BWE) of narrowband speech using Gaussian Mixture Models (GMMs). By quantifying temporal properties through information theoretic measures and using delta features, we have shown that narrowband memory significantly increases certainty about highband parameters. However, as delta f...

2004
Rongqing Huang John H. L. Hansen

The problem of unsupervised audio classification continuous to be a challenging research problem which significantly impacts ASR and Spoken Document Retrieval (SDR) performance. This paper addresses novel advances in audio classification for speech recognition. A new algorithm is proposed for audio classification, which is based on Weighted GMM Network (WGN). Two new high-level features: VSF (V...

2017
Mohamed Reda Abonazel

This paper considers the estimation methods for dynamic panel data (DPD) models with fixed effects, which suggested in econometric literature, such as least squares (LS) and generalized method of moments (GMM). These methods obtain biased estimators for DPD models. The LS estimator is inconsistent when the time dimension (T) is short regardless of the cross-sectional dimension (N). Although con...

2001
Tomoki Toda Hiroshi Saruwatari Kiyohiro Shikano

In the voice conversion algorithm based on the Gaussian Mixture Model (GMM), quality of the converted speech is degraded because the converted spectrum is exceedingly smoothed. In this paper, we newly propose the GMM-based algorithm with the Dynamic Frequency Warping (DFW) to avoid the over-smoothing. We also propose that the converted spectrum is calculated by mixing the GMM-based converted sp...

2009
Martin Graciarena Tobias Bocklet Elizabeth Shriberg Andreas Stolcke Sachin S. Kajarekar

We explore how intrinsic variations (those associated with the speaker rather than the recording environment) affect textindependent speaker verification performance. In a previous paper we introduced the SRI-FRTIV corpus and provided speaker verification results using a Gaussian mixture model (GMM) system on telephone-channel speech. In this paper we explore the use of other speaker verificati...

2006
Fuping Pan Qingwei Zhao Yonghong Yan

This paper discusses a tone pronunciation scoring system of Mandarin. It recognizes tones of syllables by using GMM model and uses the recognition results for tone assessment. Initially, experiment results are bad on strongly accented speech. There are two reasons: one is that the inaccurate force-alignment leads to incomplete F0 contours; the other is due to the special pattern of F0 contours....

2008
Anthony Larcher Jean-François Bonastre John S. D. Mason

Embedded speaker recognition in mobile devices could involve several ergonomic constraints and a limited amount of computing resources. Even if they have proved their efficiency in more classical contexts, GMM/UBM based systems show their limits in such situations, with good accuracy demanding a relatively large quantity of speech data, but with negligible harnessing of linguistic content. The ...

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