Remote monitoring services should be strongly personalised to the specific needs, preferences, abilities and motivations of elderly, a population segment whose characteristics can largely vary and even dynamically evolve over time for the same individual, depending on changing needs and usage contexts. We present a demo showing how a platform supporting End User Development (EUD) of context-dependent applications has been customized for remotely assisting elderly people at home. The user-editable personalisation features are specified by using trigger-action rules. The platform has been integrated with various sensors and appliances, and an application for remotely monitoring older adults at home. The resulting environment supports the possibility of creating trigger-action rules that can actually be executed when relevant sensors indicate that specific events or conditions occurred.

Model adaptation techniques are an efficient way to reduce the mismatch that typically occurs between the training and test condition of any speech recognizer. Adaptation techniques can usually be divided into two families of approaches. On one hand, direct model adaptation attempts to directly reestimate the model parameters, for example using MAP adaptation. Since direct adaptation only reestimates model parameters of the corresponding units appearing in the adaptation data, a large amount of such data is needed to observe any significant improvement in performance. However, nice asymptotic properties are usually observed, meaning that the performance improves as the amount of adaptation data increases. On the other hand, indirect model adaptation applies a general transformation on some clusters of model parameters. Because each individual model is transformed, the approach is quite effective when a small amount of adaptation data is available. However, as the amount of adaptation data increases, the performance improvement quickly saturates. We propose to jointly estimate model parameters and transformation parameters using a single estimation criterion based on Bayesian statistics. We show that by providing a prior distribution for the model parameters and the transformation parameters, it is possible to jointly estimate these two sets of parameters using maximum a posteriori estimation (MAP). Experimental evaluation on nonnative speaker and channel adaptation illustrates the effectiveness of the proposed approach.

Model adaptation techniques can usually be divided into indirect and direct approaches. On one hand, indirect or transformation-based techniques assume that a general transformation shared amongst different acoustic units is applied to clusters of model parameters. Such approaches (e.g. MLLR-maximum likelihood linear regression) are quite efficient when the amount of adaptation data is limited, but have poor asymptotic properties as the amount of adaptation data increases. On the other hand, direct adaptation approaches, like maximum a posteriori (MAP) estimation have nice asymptotic properties but provide only a moderate improvement when the amount of adaptation data is small. In this work, we jointly optimize a direct and indirect adaptation to take advantage of both approaches. Contrary to published approaches where direct and indirect adaptation are performed one after the other with a very loose interaction and no joint estimation criterion, we propose to estimate a MLLR-like transformation as well as the HMM mean vectors simultaneously, using a MAP estimation criterion. The optimal interaction between the direct and indirect adaptation associated with the prior knowledge provided by the MAP criterion leads to improvement over MLLR and MAP for all size of adaptation data evaluated.

We show that accurate HMMs for connected word recognition can be obtained without context dependent modeling and discriminative training. We train two HMMs for each word that have the same, standard, left to right topology with the possibility of skipping once state, but each model has a different number of states, automatically selected. The two models account for different speaking rates that occur not only in different utterances of the speakers, but also within a connected word utterance of the same speaker. This simple modeling technique has been applied to connected digit recognition using the adult speaker portion of the TI/NIST corpus giving the best results reported so far for this database. It has also been tested on telephone speech using long sequences of Italian digits (credit card numbers), giving better results with respect to classical models with a larger number of densities.

In the past few years, transformation-based model adaptation techniques have been widely used to help reducing acoustic mismatch between training and testing conditions of automatic speech recognizers. The estimation of the transformation parameters is usually carried out using estimation paradigms based on classical statistics such as maximum likelihood, mainly because of their conceptual and computational simplicity. However, it appears necessary to introduce some constraints on the possible values of the transformation parameters to avoid getting unreasonable estimates that might perturb the underlying structure of the acoustic space. In this paper, we propose to introduce such constraints using Bayesian statistics, where a prior distribution of the transformation parameters is used. A Bayesian counterpart of the well known maximum likelihood linear regression (MLLR) adaption is formulated based on maximum a posteriori (MAP) estimation. Supervised, unsupervised and incremental non-native speaker adaptation experiments are carried out to compare the proposed MAPLR approach to MLLR. Experimental results show that MAPLR outperforms MLLR.

This paper proposes the optimization of a non-standard objective function in the framework of maximum mutual information estimation (MMIE). In contrast with the classical MMIE estimation, where only misrecognized training utterances contribute to the optimization process, the contributions of near-miss classifications are naturally embedded in the maximization of the proposed function because it takes into account a non-linear combination of the probabilities of the competing models that can be tuned by means of a single parameter. This corrective training procedure has been applied to an isolated word recognition task leading to significant performance improvements with respect to maximum likelihood estimation and MMIE.

In this paper we show that accurate HMMs for connected word recognition can be obtained without context dependent modeling and discriminative training. To account for di erent speaking rates, we de ne two HMMs for each word that must be trained. The two models have the same, standard, left to right topology with the possibility of skipping one state, but each model has a di erent number of states, automatically selected. Our simple modeling and training technique has been applied to connected digit recognition using the adult speaker portion of the TI/NIST corpus. The obtained results are comparable with the best ones reported in the literature for models with a larger number of densities.