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Speech analytics

Known as: LVCSR 
Speech analytics is the process of analyzing recorded calls to gather customer information to improve communication and future interaction. The… Expand
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Papers overview

Semantic Scholar uses AI to extract papers important to this topic.
Highly Cited
2016
Highly Cited
2016
Many state-of-the-art Large Vocabulary Continuous Speech Recognition (LVCSR) Systems are hybrids of neural networks and Hidden… Expand
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Highly Cited
2015
Highly Cited
2015
Recurrent neural network architectures have been shown to efficiently model long term temporal dependencies between acoustic… Expand
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Highly Cited
2013
Highly Cited
2013
Recently, pre-trained deep neural networks (DNNs) have outperformed traditional acoustic models based on Gaussian mixture models… Expand
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Highly Cited
2013
Highly Cited
2013
Convolutional Neural Networks (CNNs) are an alternative type of neural network that can be used to reduce spectral variations and… Expand
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Highly Cited
2013
Highly Cited
2013
While Deep Neural Networks (DNNs) have achieved tremendous success for large vocabulary continuous speech recognition (LVCSR… Expand
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Review
2012
Review
2012
Most current speech recognition systems use hidden Markov models (HMMs) to deal with the temporal variability of speech and… Expand
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Review
2007
Review
2007
Hidden Markov Models (HMMs) provide a simple and effective framework for modelling time-varying spectral vector sequences. As a… Expand
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Highly Cited
2007
Highly Cited
2007
In recent years, probabilistic features became an integral part of state-of-the-are LVCSR systems. In this work, we are exploring… Expand
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Highly Cited
2001
Highly Cited
2001
Abstract With the distribution of speech technology products all over the world, the portability to new target languages becomes… Expand
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Highly Cited
2000
Highly Cited
2000
We describe a statistical approach for modeling dialogue acts in conversational speech, i.e., speech-act-like units such as… Expand