Jean-Marc Boite

Optimizing recognition and rejection performance in wordspotting systems

Compares the performance which can be achieved by different hidden Markov model (HMM) based wordspotting techniques when their parameters are tuned to optimize recognition and rejection rates. An alternative approach which does not attempt to explicitly model extraneous speech or non-speech noise is also proposed. After optimization of each of these approaches, it appears that the proposed version performs at least as well as the other methods with the advantage of simplicity and possibility to be used in hybrid models using HMMs with a multilayer perceptron (MLP). Test results are reported on a speaker independent telephone database containing 10 keywords as well as on the speaker independent ARPA resource management database in which between 10 and 250 keywords were defined.<<ETX>>

Hybrid HMM/ANN systems for training independent tasks: experiments on Phonebook and related improvements

In this paper, we evaluate multi-Gaussian HMM systems and hybrid HMM/ANN systems in the framework of task independent training for small size (75 words) and medium size (600 words) vocabularies. To do this, we use the Phonebook database (Pitrelli et al., 1995) which is particularly well suited to this kind of experiment since (1) it is a very large telephone database and (2) the size and content of the test vocabulary is very flexible. For each system, different HMM topologies are compared to test the influence of state tying (with a number of parameters approximately kept constant) on the recognition performance. Two lexica (Phonebook and CMU) are also compared and it is shown that the CMU lexicon leads to significantly better performance. Finally, it is shown that with a quite simple system and a few adaptations to the basic HMM/ANN scheme, recognition performance of 98.5% and 94.7% can easily be achieved, respectively on a lexicon of 75 and 600 words (isolated words, telephone speech and lexicon words not present in the training data).

Task independent and dependent training: performance comparison of HMM and hybrid HMM/MLP approaches

Compares speaker independent isolated word recognition performance obtained with standard phonemic hidden Markov models (HMMs) and hybrid approaches using a multilayer perceptron (MLP) to estimate the HMM emission probabilities. This latter approach has previously been shown particularly effective on a large vocabulary, speaker independent, continuous speech recognition task (i.e., ARPA Resource Management) by using simple context-independent phoneme models and single pronunciation word models. As a consequence, the main goal of the paper is to compare the performance which can be achieved by the different approaches for both task dependent and independent training.<<ETX>>