Context Independent And Context Dependent Hybrid HMM/ANN Systems For Vocabulary Independent Tasks

In this paper, hybrid HMM/ANN systems are used to model context dependent phones. In order to reduce the number of parameters as well as to better catch the dynamics of the phonetic segments, we combine (context dependent) diphone models with context independent phone models. Transitions from phone to phone are modeled as generalized context dependent distributions while phonetic units are context independent models trained on the less coarticulated middle part of each phone. Words are thus modeled as a sequence of probability distributions alternatively representing the middle part of the phonemes and the transitions from phone to phone. A single neural network is used to estimate both context independent phone probabilities and generalized context dependent diphone (phone to phone transition) probabilities. Resulting systems are compared to classical context independent phone-based HMM/ANN systems with the same number of parameters. The Phonebook isolated word database has been used for training the systems. Testing is done on small (75 words), medium (600 words) and large (8000 words) lexicons. Test words were not present in the training vocabulary

Hybrid HMM/ANN Systems for Speaker Independent Continuous Speech Recognition in French

In this paper we report a series of tests carried out on our hybrid HMM/ANN systems which aims at combining Neural Networks theory and Hidden Markov Models (HMMs) for speech recognition of a continuous speech French database: BREF-80. As this database is not manually labelled, we describe a new method based on the temporal alignment of the speech signal on a high quality synthetic speech pattern to generate a first segmentation in order to bootstrap the training procedure. A phone recognition experiment with our baseline system achieved a phone accuracy of about 63% which is, to our knowledge, the best result reported in the litterature. Preliminary experiments on continuous speech recognition have set a baseline performance for our hybrid HMM/ANN system on BREF using 1K, 3K and 13 K word lexicons. All the experiments were carried out with the STRUT (Speech Training and Recognition Unified Toolkit) software [10]. I. Introduction Significant advances have been made in recent years in ..

Genomic Analysis of Colombian Leishmania panamensis strains with different level of virulence

Abstract The establishment of Leishmania infection in mammalian hosts and the subsequent manifestation of clinical symptoms require internalization into macrophages, immune evasion and parasite survival and replication. Although many of the genes involved in these processes have been described, the genetic and genomic variability associated to differences in virulence is largely unknown. Here we present the genomic variation of four Leishmania (Viannia) panamensis strains exhibiting different levels of virulence in BALB/c mice and its application to predict novel genes related to virulence. De novo DNA sequencing and assembly of the most virulent strain allowed comparative genomics analysis with sequenced L. (Viannia) panamensis and L. (Viannia) braziliensis strains, and showed important variations at intra and interspecific levels. Moreover, the mutation detection and a CNV search revealed both base and structural genomic variation within the species. Interestingly, we found differences in the copy number and protein diversity of some genes previously related to virulence. Several machine-learning approaches were applied to combine previous knowledge with features derived from genomic variation and predict a curated set of 66 novel genes related to virulence. These genes can be prioritized for validation experiments and could potentially become promising drug and immune targets for the development of novel prophylactic and therapeutic interventions