There is substantial neural evidence for the role of morphology (word-internal structure) in visual word recognition. We extend this work to auditory word recognition, drawing on recent evidence that phoneme prediction is central to this process. In a magnetoencephalography (MEG) study, we crossed morphological complexity (bruis-er vs. bourbon) with the predictability of the word ending (bourbo...

This work presents training methods and recogni tion experiments for phoneme wise tied mixture den sities in hidden Markov models HMM The system trains speaker dependent but vocabulary independent phoneme models for the recognition of Finnish words The Learning Vector Quantization LVQ methods are applied to increase the discrimination between the phoneme models A segmental LVQ training is pro p...

In HMM-based recognition systems for large vocabulary, the observation likelihoods provided by the acoustic models are useful in confidence measures if they are properly normalised. This paper compares two normalisation methods for the acoustic model likelihoods: unconstrained normalisation, based on the unconditional observation likelihood, and constrained normalisation, based on the observati...

We investigate and compare several techniques for automatic recognition of unconstrained context-independent phoneme strings from TIMIT and NTIMIT databases. Among the compared techniques, the technique based on TempoRAl Patterns (TRAP) achieves the best results in the clean speech, it achieves about 10% relative improvements against baseline system. Its advantage is also observed in the presen...

Through computational modeling, here we examine whether visual and task characteristics of writing systems alone can account for lateralization differences in visual word recognition between different languages without assuming influence from left hemisphere (LH) lateralized language processes. We apply a hemispheric processing model of face recognition to visual word recognition; the model imp...

We present a Modular Neural Network (MNN) for phoneme recognition within the framework of a hybrid system (neural networks and HMMs) for speakerindependent single word recognition. With this approach, we are taking the computational effort into account which is used as an additional criterion for assessing the system performance. The main idea of the proposed MNN is the distribution of the comp...

The genetic and environmental etiologies of individual differences in printed word recognition and related skills were explored in 440 identical and fraternal twin pairs between 8 and 18 years of age. A theoretically driven measurement model identified five latent variables: IQ, phoneme awareness, word recognition, phonological decoding, and orthographic coding. Cholesky decomposition models on...

In this paper, we describe a method to enhance the readability of out-of-vocabulary items (OOVs) in the textual output in a large vocabulary continuous speech recognition system. The basic idea is to indicate uncertain words in the transcriptions and replace them with phoneme recognition results that are post-processed using a phoneme-to-grapheme (P2G) converter. We concentrate on the final ste...