On-line adaptive learning of the continuous density hidden Markov model based on approximate recursive Bayes estimate

We present a framework of quasi-Bayes (QB) learning of the parameters of the continuous density hidden Markov model (CDHMM) with Gaussian mixture state observation densities. The QB formulation is based on the theory of recursive Bayesian inference. The QB algorithm is designed to incrementally update the hyperparameters of the approximate posterior distribution and the CDHMM parameters simultaneously. By further introducing a simple forgetting mechanism to adjust the contribution of previously observed sample utterances, the algorithm is adaptive in nature and capable of performing an online adaptive learning using only the current sample utterance. It can, thus, be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, and transducers. As an example, the QB learning framework is applied to on-line speaker adaptation and its viability is confirmed in a series of comparative experiments using a 26-letter English alphabet vocabulary.published_or_final_versio

A study of prior sensitivity for Bayesian predictive classificationbased robust speech recognition

We previously introduced a new Bayesian predictive classification (BPC) approach to robust speech recognition and showed that the BPC is capable of coping with many types of distortions. We also learned that the efficacy of the BPC algorithm is influenced by the appropriateness of the prior distribution for the mismatch being compensated. If the prior distribution fails to characterize the variability reflected in the model parameters, then the BPC will not help much. We show how the knowledge and/or experience of the interaction between the speech signal and the possible mismatch guide us to obtain a better prior distribution which improves the performance of the BPC approach.published_or_final_versio

A study of prior sensitivity for Bayesian predictive classificationbased robust speech recognition

We previously introduced a new Bayesian predictive classification (BPC) approach to robust speech recognition and showed that the BPC is capable of coping with many types of distortions. We also learned that the efficacy of the BPC algorithm is influenced by the appropriateness of the prior distribution for the mismatch being compensated. If the prior distribution fails to characterize the variability reflected in the model parameters, then the BPC will not help much. We show how the knowledge and/or experience of the interaction between the speech signal and the possible mismatch guide us to obtain a better prior distribution which improves the performance of the BPC approach.published_or_final_versio

On-line adaptive learning of the correlated continuous density hidden Markov models for speech recognition

We extend our previously proposed quasi-Bayes adaptive learning framework to cope with the correlated continuous density hidden Markov models (HMMs) with Gaussian mixture state observation densities in which all mean vectors are assumed to be correlated and have a joint prior distribution. A successive approximation algorithm is proposed to implement the correlated mean vectors' updating. As an example, by applying the method to an on-line speaker adaptation application, the algorithm is experimentally shown to be asymptotically convergent as well as being able to enhance the efficiency and the effectiveness of the Bayes learning by taking into account the correlation information between different model parameters. The technique can be used to cope with the time-varying nature of some acoustic and environmental variabilities, including mismatches caused by changing speakers, channels, transducers, environments, and so on.published_or_final_versio

On adaptive decision rules and decision parameter adaptation for automatic speech recognition

Recent advances in automatic speech recognition are accomplished by designing a plug-in maximum a posteriori decision rule such that the forms of the acoustic and language model distributions are specified and the parameters of the assumed distributions are estimated from a collection of speech and language training corpora. Maximum-likelihood point estimation is by far the most prevailing training method. However, due to the problems of unknown speech distributions, sparse training data, high spectral and temporal variabilities in speech, and possible mismatch between training and testing conditions, a dynamic training strategy is needed. To cope with the changing speakers and speaking conditions in real operational conditions for high-performance speech recognition, such paradigms incorporate a small amount of speaker and environment specific adaptation data into the training process. Bayesian adaptive learning is an optimal way to combine prior knowledge in an existing collection of general models with a new set of condition-specific adaptation data. In this paper, the mathematical framework for Bayesian adaptation of acoustic and language model parameters is first described. Maximum a posteriori point estimation is then developed for hidden Markov models and a number of useful parameters densities commonly used in automatic speech recognition and natural language processing.published_or_final_versio

A Bayesian predictive classification approach to robust speech recognition

We introduce a new Bayesian predictive classification (BPC) approach to robust speech recognition and apply the BPC framework to Gaussian mixture continuous density hidden Markov model based speech recognition. We propose and focus on one of the approximate BPC approaches called quasi-Bayesian predictive classification (QBPC). In comparison with the standard plug-in maximum a posteriori decoding, when the QBPC method is applied to speaker independent recognition of a confusable vocabulary namely 26 English letters, where a broad range of mismatches between training and testing conditions exist, the QBPC achieves around 14% relative recognition error rate reduction. While the QBPC method is applied to cross-gender testing on a less confusable vocabulary, namely 20 English digits and commands, the QBPC method achieves around 24% relative recognition error rate reduction.published_or_final_versio

On-line adaptation of the SCHMM parameters based on the segmental quasi-bayes learning for speech recognition

On-line quasi-Bayes adaptation of the mixture coefficients and mean vectors in semicontinuous hidden Markov model (SCHMM) is studied. The viability of the proposed algorithm is confirmed and the related practical issues are addressed in a specific application of on-line speaker adaptation using a 26-word English alphabet vocabulary.published_or_final_versio

Bayesian adaptive learning of the parameters of hidden Markov model for speech recognition

A theoretical framework for Bayesian adaptive training of the parameters of a discrete hidden Markov model (DHMM) and of a semi-continuous HMM (SCHMM) with Gaussian mixture state observation densities is presented. In addition to formulating the forward-backward MAP (maximum a posteriori) and the segmental MAP algorithms for estimating the above HMM parameters, a computationally efficient segmental quasi-Bayes algorithm for estimating the state-specific mixture coefficients in SCHMM is developed. For estimating the parameters of the prior densities, a new empirical Bayes method based on the moment estimates is also proposed. The MAP algorithms and the prior parameter specification are directly applicable to training speaker adaptive HMMs. Practical issues related to the use of the proposed techniques for HMM-based speaker adaptation are studied. The proposed MAP algorithms are shown to be effective especially in the cases in which the training or adaptation data are limited.published_or_final_versio

Transplanted Olfactory Ensheathing Cells Reduce Retinal Degeneration in Royal College of Surgeons Rats

PURPOSE OF THE STUDY: Retinitis pigmentosa (RP) is a group of genetic disorders and a slow loss of vision that is caused by a cascade of retinal degenerative events. We examined whether these retinal degenerative events were reduced after cultured mixtures of adult olfactory ensheathing cells (OECs) and olfactory nerve fibroblasts (ONFs) were transplanted into the subretinal space of 1-month-old RCS rat, a classic model of RP. MATERIALS AND METHODS: The changes in retinal photoreceptors and Müller cells of RCS rats after cell transplantation were observed by the expression of recoverin and glial fibrillary acidic protein (GFAP), counting peanut agglutinin (PNA)-positive cone outer segments and calculating the relative apoptotic area. The retinal function was also evaluated by Flash electroretinography (ERG). To further investigate the mechanisms, by which OECs/ONFs play important roles in the transplanted retinas, nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF), and basic fibroblast growth factor (bFGF) secretion of the cultured cells were analyzed by ELISA. The ability of OECs/ONFs to ingest porcine retinal outer segments and the amount of phagocytosis were compared with retinal pigment epithelium (RPE) cells. RESULTS: Our research showed that the transplantation of OECs/ONFs mixtures restored recoverin expression, protected retinal outer segments, increased PNA-positive cone outer segments, reduced caspase-positive apoptotic figures, downregulated GFAP, and maintained the b-wave of the ERG. Cultured OECs/ONFs expressed and secreted NGF, BDNF, and bFGF which made contributions to assist survival of the photoreceptors. An in vitro phagocytosis assay showed that OECs, but not ONFs, phagocytosed porcine retinal outer segments, and the phagocytic ability of OECs was even superior to that of RPE cells. CONCLUSIONS: These findings demonstrate that transplantation of OECs/ONFs cleaned up the accumulated debris in subretinal space, and provided an intrinsic continuous supply of neurotrophic factors. It suggested that transplantation of OECs/ONFs might be a possible future route for protection of the retina and reducing retinal degeneration in RP

Micro-manufacturing : research, technology outcomes and development issues

Besides continuing effort in developing MEMS-based manufacturing techniques, latest effort in Micro-manufacturing is also in Non-MEMS-based manufacturing. Research and technological development (RTD) in this field is encouraged by the increased demand on micro-components as well as promised development in the scaling down of the traditional macro-manufacturing processes for micro-length-scale manufacturing. This paper highlights some EU funded research activities in micro/nano-manufacturing, and gives examples of the latest development in micro-manufacturing methods/techniques, process chains, hybrid-processes, manufacturing equipment and supporting technologies/device, etc., which is followed by a summary of the achievements of the EU MASMICRO project. Finally, concluding remarks are given, which raise several issues concerning further development in micro-manufacturing