In this paper, we investigate the use of confidence measures for the evaluation of pronunciation models and the employment of these evaluations in an automatic baseform learning process. The confidence measures and pronunciation models are obtained from the ABBOT hybrid Hidden Markov Model/Artificial Neural Network (HMM/ANN) Large Vocabulary Continuous Speech Recognition (LVCSR) system [8]. Experiments were carried out for a number of baseform learning schemes using the ARPA North American Business News (NAB) and the Broadcast News (BN) corpora from which it was found that a confidence measure based scheme provided the largest reduction in Word Error Rate (WER)
In this paper we define a number of confidence measures derived from an acceptor HMM and evaluate th...
In automatic speech recognition, confidence measures aim at estimating the confidence we can give to...
Generally, the user group of a language is remarkably diverse in terms of speaker-specific character...
In this paper we introduce four acoustic confidence measures which are derived from the output of a ...
In this paper we introduce a set of related confidence measures for large vocabulary continuous spee...
In this paper we introduce a set of related confidence measures for large vocabulary continuous spee...
In this paper we introduce four acoustic confidence measures which are derived from the output of a ...
In this paper we define a number of confidence measures derived from an acceptor HMM and evaluate th...
Confidence measures have been found to be useful for a number tasks within the field of Automatic Sp...
In this paper we introduce a set of related confidence measures for large vocabulary continuous spee...
This paper describes three experiments in using frame level observation probabilities as the basis f...
In this paper we define an acoustic confidence measure based on the estimates of local posterior pro...
In automatic speech recognition, confidence measures aim at estimating the confidence we can give to...
In the context of large vocabulary speech recognition system, it’s of major interest to classify eve...
Despite the significant advances in speech and language technologies speech recognition systems are ...
In this paper we define a number of confidence measures derived from an acceptor HMM and evaluate th...
In automatic speech recognition, confidence measures aim at estimating the confidence we can give to...
Generally, the user group of a language is remarkably diverse in terms of speaker-specific character...
In this paper we introduce four acoustic confidence measures which are derived from the output of a ...
In this paper we introduce a set of related confidence measures for large vocabulary continuous spee...
In this paper we introduce a set of related confidence measures for large vocabulary continuous spee...
In this paper we introduce four acoustic confidence measures which are derived from the output of a ...
In this paper we define a number of confidence measures derived from an acceptor HMM and evaluate th...
Confidence measures have been found to be useful for a number tasks within the field of Automatic Sp...
In this paper we introduce a set of related confidence measures for large vocabulary continuous spee...
This paper describes three experiments in using frame level observation probabilities as the basis f...
In this paper we define an acoustic confidence measure based on the estimates of local posterior pro...
In automatic speech recognition, confidence measures aim at estimating the confidence we can give to...
In the context of large vocabulary speech recognition system, it’s of major interest to classify eve...
Despite the significant advances in speech and language technologies speech recognition systems are ...
In this paper we define a number of confidence measures derived from an acceptor HMM and evaluate th...
In automatic speech recognition, confidence measures aim at estimating the confidence we can give to...
Generally, the user group of a language is remarkably diverse in terms of speaker-specific character...