. In this paper, building upon previous work by others [7], 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...

Confidence measures represent a systematic way to express reliability of speech recognition results. A common approach to confidence measuring is to take profit of the information that several recognition-related features offer and to combine them, through a given compilation mechanism, into a more effective way to distinguish between correct and incorrect recognition results. We propose to use a fuzzy reasoning scheme to perform the information compilation step. Our approach opposes the previously proposed ones because ours treats the uncertainty of recognition hypotheses in terms of "possibility" contrasted to the "probability" notion of similar works. Experimental results over isolated words, continuous speech and keyword spotting recognition tasks show higher performance of our system compared against standard compilation methods. Here we demonstrate that, due to their approach to uncertainty; to their capabilities to handle expert knowledge and to their versatility, Fuzzy Inferenc...

Word level confidence measures are of use in many areas of speech recognition. Comparing the hypothesized word score to the score of a `filler' model has been the most popular confidence measure because it is highly efficient, and does not require a large amount of training data. This paper explores an extension of this technique which also compares the hypothesized word score to the scores of words that are commonly confused for it, while maintaining efficiency and the low demand for training data. The proposed method gives a 39% relative false accept rate reduction over the `filler'- model baseline, at a false reject rate of 5%. 1. Introduction Confidence measures are useful in many aspects of speech recognition, including supervised and unsupervised adaptation, recognition error rejection, out-of-vocabulary word detection, and keyword spotting. A method that has been popular for word-based confidence modeling is the comparison of the score of the hypothesized word with the score o...

We describe several new research directions we investigated toward the development of our broadcast news transcription system for the 1998 DARPA H4 evaluations. Our goal was to develop significantly faster and smaller speech recognition systems without degrading the word error rate of our 1997 system. We did this through significant algorithmic research creating various new techniques. A sample of these techniques was used to put together our 1998 broadcast news system, which is conceptually much simpler, faster, and smaller, but gives the same word error rate as our 1997 system. In particular, our 1998 system is based on a simple phonetically tied mixture (PTM) model with a total of only 13,000 Gaussians, as compared to a 67,000-Gaussian state-clustered system we used in 1997. 1. Introduction One of our main goals in 1998 was to significantly increase speed and decrease model size, while maintaining or improving accuracy. These goals are difficult to achieve simultaneously because o...

Confidence measures have been found to be useful for a number tasks within the field of Automatic Speech Recognition (ASR). For example, the use of confidence measures has been reported in the utterance verification, keyword spotting and Out-of-Vocabulary (OOV) word spotting literature. In this report, it is shown that so called 'hybrid Artificial Neural Network/Hidden Markov Model' (HMM/ANN) systems are well suited to the task of generating confidence measures, due to their ability to provide local phone class posterior probability estimates which may be used to generate confidence measures in a computationally efficient manner. A number of evaluation metrics are also described and the performance of five confidence measures derived from the ABBOT hybrid HMM/ANN system for the tasks of utterance verification and OOV word spotting are evaluated using these metrics. Besides the tasks described above, confidence measures may also be used for tasks such as filtering the acoustics for a nu...