Publication

Improving Posterior Based Confidence Measures in Hybrid HMM/ANN Speech Recognition Systems

Hervé Bourlard
1998
Reports

Abstract

In this paper we define and investigate a set of confidence measures based on hybrid Hidden Markov Model/Artificial Neural Network (HMM/ANN) acoustic models. All these measures are using the neural network to estimate the local phone posterior probabilities, which are then combined and normalized in different ways. Experimental results will indeed show that the use of an appropriate duration normalization is very important to obtain good estimates of the phone and word confidences. The different measures are evaluated at the phone and word levels on both an isolated word task (PHONEBOOK) and a continuous speech recognition task (BREF). It will be shown that one of those confidence measures is well suited for utterance verification, and that (as one could expect) confidence measures at the word level perform better than those at the phone level. Finally, using the resulting approach on PHONEBOOK to rescore the N-best list is shown to yield a 34% decrease in word error rate.

Official source

https://infoscience.epfl.ch/entities/publication/b22049e8-8d00-43f5-8e9a-1b68fb5bbb3a

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Hervé Bourlard
1998
Reports

Abstract

Official source

https://infoscience.epfl.ch/entities/publication/b22049e8-8d00-43f5-8e9a-1b68fb5bbb3a

About this result

Related concepts (32)

Confidence interval

In frequentist statistics, a confidence interval (CI) is a range of estimates for an unknown parameter. A confidence interval is computed at a designated confidence level; the 95% confidence level is most common, but other levels, such as 90% or 99%, are sometimes used. The confidence level, degree of confidence or confidence coefficient represents the long-run proportion of CIs (at the given confidence level) that theoretically contain the true value of the parameter; this is tantamount to the nominal coverage probability.

Speech recognition

Speech recognition is an interdisciplinary subfield of computer science and computational linguistics that develops methodologies and technologies that enable the recognition and translation of spoken language into text by computers. It is also known as automatic speech recognition (ASR), computer speech recognition or speech to text (STT). It incorporates knowledge and research in the computer science, linguistics and computer engineering fields. The reverse process is speech synthesis.

Robust measures of scale

In statistics, robust measures of scale are methods that quantify the statistical dispersion in a sample of numerical data while resisting outliers. The most common such robust statistics are the interquartile range (IQR) and the median absolute deviation (MAD). These are contrasted with conventional or non-robust measures of scale, such as sample standard deviation, which are greatly influenced by outliers.

Related publications (32)

Improving Posterior Based Confidence Measures in Hybrid HMM/ANN Speech Recognition Systems

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