Add to ORKGWe describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixture Model structure, and the means and mixture weights vary in a subspace of the total parameter space. We call this a Subspace Gaussian Mixture Model (SGMM). Globally shared parameters define the subspace. This style of acous-tic model allows for a much more compact representation and gives better results than a conventional modeling approach, particularl
Acoustic model parameter estimation is hampered by a lack of data. To reduce the number of parameter...
Abstract—We investigate cross-lingual acoustic modelling for low resource languages using the subspa...
This paper investigates the impact of subspace based techniques for acoustic modeling in automatic s...
We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixtu...
We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixtu...
We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states shar...
ABSTRACT This document describes an extension of the Subspace Gaussian Mixture Model (SGMM). The ext...
This paper describes experimental results of applying Subspace Gaussian Mixture Models (SGMMs) in tw...
This paper investigates employment of Subspace Gaussian Mixture Models (SGMMs) for acoustic model ad...
Although research has previously been done on multilingual speech recognition, it has been found to ...
The subspace Gaussian mixture model (SGMM) has been exploited for cross-lingual speech recognition. ...
This paper provides an overview of Gaussian Mixture Model (GMM) and its component of speech signal. ...
Last year we introduced the Subspace Gaussian Mixture Model (SGMM), and we demonstrated Word Error R...
The subspace Gaussian mixture model (SGMM) has been recently proposed as an acoustic modeling techni...
This paper studies cross-lingual acoustic modeling in the context of subspace Gaussian mixture model...
Acoustic model parameter estimation is hampered by a lack of data. To reduce the number of parameter...
Abstract—We investigate cross-lingual acoustic modelling for low resource languages using the subspa...
This paper investigates the impact of subspace based techniques for acoustic modeling in automatic s...
We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixtu...
We describe an acoustic modeling approach in which all phonetic states share a common Gaussian Mixtu...
We describe a new approach to speech recognition, in which all Hidden Markov Model (HMM) states shar...
ABSTRACT This document describes an extension of the Subspace Gaussian Mixture Model (SGMM). The ext...
This paper describes experimental results of applying Subspace Gaussian Mixture Models (SGMMs) in tw...
This paper investigates employment of Subspace Gaussian Mixture Models (SGMMs) for acoustic model ad...
Although research has previously been done on multilingual speech recognition, it has been found to ...
The subspace Gaussian mixture model (SGMM) has been exploited for cross-lingual speech recognition. ...
This paper provides an overview of Gaussian Mixture Model (GMM) and its component of speech signal. ...
Last year we introduced the Subspace Gaussian Mixture Model (SGMM), and we demonstrated Word Error R...
The subspace Gaussian mixture model (SGMM) has been recently proposed as an acoustic modeling techni...
This paper studies cross-lingual acoustic modeling in the context of subspace Gaussian mixture model...
Acoustic model parameter estimation is hampered by a lack of data. To reduce the number of parameter...
Abstract—We investigate cross-lingual acoustic modelling for low resource languages using the subspa...
This paper investigates the impact of subspace based techniques for acoustic modeling in automatic s...