Multilayer Perceptron Based Hierarchical Acoustic Modeling for Automatic Speech Recognition
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One major research challenge in the domain of the analysis of meeting room data is the automatic transcription of what is spoken during meetings, a task which has gained considerable attention within the ASR research community through the NIST rich transcr ...
In this paper we present a study of automatic speech recognition systems using context-dependent phonemes and graphemes as sub-word units based on the conventional HMM/GMM system as well as tandem system. Experimental studies conducted on three different c ...
We propose an alternative means of training a multilayer perceptron for the task of speech activity detection based on a criterion to minimise the error in the estimation of mean and variance statistics for speech cepstrum based features using the Kullback ...
We address the problem of clustering multimodal group actions in meetings using a two-layer HMM framework. Meetings are structured as sequences of group actions. Our approach aims at creating one cluster for each group action, where the number of group act ...
The use of large speech corpora in example-based approaches for speech recognition is mainly focused on increasing the number of examples. This strategy presents some difficulties because databases may not provide enough examples for some rare words. In th ...
One major research challenge in the domain of the analysis of meeting room data is the automatic transcription of what is spoken during meetings, a task which has gained considerable attention within the ASR research community through the NIST rich transcr ...
Juicer is a decoder for HMM-based large vocabulary speech recognition that uses a weighted finite state transducer (WFST) representation of the search space. The package consists of a number of command line utilities: the Juicer decoder itself, along with ...
In recent papers, entropy computed from sub-bands of the spectrum was used as a feature for automatic speech recognition. In the present paper, we further study the sub-band spectral entropy features which can give the flatness/peakiness of the sub-band sp ...
We address the problem of clustering multimodal group actions in meetings using a two-layer HMM framework. Meetings are structured as sequences of group actions. Our approach aims at creating one cluster for each group action, where the number of group act ...
The use of large speech corpora in example-based approaches for speech recognition is mainly focused on increasing the number of examples. This strategy presents some difficulties because databases may not provide enough examples for some rare words. In th ...
