Add to ORKGAutomatic Speech Recognition (ASR) systems utilize statistical acoustic and language models to find the most probable word sequence when the speech signal is given. Hidden Markov Models (HMMs) are used as acoustic models and language model probabilities are approximated using n-grams where the probability of a word is conditioned on n-1 previou
This thesis investigates a stochastic modeling approach to word hypothesis of phonetic strings for a...
The field of Automatic Speech Recognition (ASR) is about 60 years old. There have been many interest...
Standard hidden Markov model (HMM) based automatic speech recognition (ASR) systems use phonemes as ...
The general subject of this work is to present mathematical methods encountered in auto-matic speech...
Standard hidden Markov model (HMM) based automatic speech recogni-tion (ASR) systems use phonemes as...
Natural language processing enables computer and machines to understand and speak human languages. S...
The demand of intelligent machines that may recognize the spoken speech and respond in a natural vo...
Abstract. The aim of this overview4 is to describe major approaches and trends used for statistical ...
One of the key challenges involved in building statistical automatic speech recog-nition (ASR) syste...
This thesis introduces an autoregressive hidden Markov model (HMM) and demonstrates its application ...
Automatic language identification is one of the important topics in multilingual speech technology. ...
A novel self-supervised discriminative training method for estimating language models for automatic ...
Kullback-Leibler divergence based hidden Markov model (KL-HMM) is an approach where a posteriori pro...
Spoken words convey several levels of information. At the primary level, the speech conveys words or...
The move towards larger vocabulary Automatic Speech Recognition (ASR) systems places greater demands...
This thesis investigates a stochastic modeling approach to word hypothesis of phonetic strings for a...
The field of Automatic Speech Recognition (ASR) is about 60 years old. There have been many interest...
Standard hidden Markov model (HMM) based automatic speech recognition (ASR) systems use phonemes as ...
The general subject of this work is to present mathematical methods encountered in auto-matic speech...
Standard hidden Markov model (HMM) based automatic speech recogni-tion (ASR) systems use phonemes as...
Natural language processing enables computer and machines to understand and speak human languages. S...
The demand of intelligent machines that may recognize the spoken speech and respond in a natural vo...
Abstract. The aim of this overview4 is to describe major approaches and trends used for statistical ...
One of the key challenges involved in building statistical automatic speech recog-nition (ASR) syste...
This thesis introduces an autoregressive hidden Markov model (HMM) and demonstrates its application ...
Automatic language identification is one of the important topics in multilingual speech technology. ...
A novel self-supervised discriminative training method for estimating language models for automatic ...
Kullback-Leibler divergence based hidden Markov model (KL-HMM) is an approach where a posteriori pro...
Spoken words convey several levels of information. At the primary level, the speech conveys words or...
The move towards larger vocabulary Automatic Speech Recognition (ASR) systems places greater demands...
This thesis investigates a stochastic modeling approach to word hypothesis of phonetic strings for a...
The field of Automatic Speech Recognition (ASR) is about 60 years old. There have been many interest...
Standard hidden Markov model (HMM) based automatic speech recognition (ASR) systems use phonemes as ...