Add to ORKGThis paper presents a new time/memory-efficient algorithm for the evaluation of state likelihoods in an HMM-based speech recognizer where the states are modeled by Gaussian Mixtures. We first present a fast hierarchical labeling scheme and then an improved version, which is specifically geared toward use in recognizers that use asynchronous search (e.g., stack search) as opposed to synchronous (Viterbi) search
This paper presents methods to improve the probability density estimation in hidden Markov models fo...
A summary of the theory of the hybrid connectionist HMM (hidden Markov model) continuous speech reco...
Resumo: Atualmente os sistemas de reconhecimento de fala baseados em HMMs são utilizados em diversas...
International audienceLVCSR systems are usually based on continuous density HMMs, which are typicall...
LVCSR systems are usually based on continuous density HMMs, which are typically implemented using Ga...
International audienceA fast likelihood computation approach called dynamic Gaussian selection (DGS)...
For Automatic Speech Recognition ASR systems using continuous Hidden Markov Models (HMMs), the compu...
This paper studies algorithms for reducing the com-putational eort of the mixture density calculatio...
The Self-Organizing Map (SOM) is widely applied for data clustering and visualization. In this paper...
Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech...
Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech...
We describe a sub-vector clustering technique to reduce the memory size and computational cost of co...
Summarization: An algorithm is proposed that achieves a good tradeoff between modeling resolution an...
In an HMM based large vocabulary continuous speech recognition system, the evaluation of - context d...
Large vocabulary speech recognition systems based on hidden Markov models (HMM) make use of many ten...
This paper presents methods to improve the probability density estimation in hidden Markov models fo...
A summary of the theory of the hybrid connectionist HMM (hidden Markov model) continuous speech reco...
Resumo: Atualmente os sistemas de reconhecimento de fala baseados em HMMs são utilizados em diversas...
International audienceLVCSR systems are usually based on continuous density HMMs, which are typicall...
LVCSR systems are usually based on continuous density HMMs, which are typically implemented using Ga...
International audienceA fast likelihood computation approach called dynamic Gaussian selection (DGS)...
For Automatic Speech Recognition ASR systems using continuous Hidden Markov Models (HMMs), the compu...
This paper studies algorithms for reducing the com-putational eort of the mixture density calculatio...
The Self-Organizing Map (SOM) is widely applied for data clustering and visualization. In this paper...
Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech...
Acoustic modeling using mixtures of multivariate Gaussians is the prevalent approach for many speech...
We describe a sub-vector clustering technique to reduce the memory size and computational cost of co...
Summarization: An algorithm is proposed that achieves a good tradeoff between modeling resolution an...
In an HMM based large vocabulary continuous speech recognition system, the evaluation of - context d...
Large vocabulary speech recognition systems based on hidden Markov models (HMM) make use of many ten...
This paper presents methods to improve the probability density estimation in hidden Markov models fo...
A summary of the theory of the hybrid connectionist HMM (hidden Markov model) continuous speech reco...
Resumo: Atualmente os sistemas de reconhecimento de fala baseados em HMMs são utilizados em diversas...