Phonemic or phonetic sub-word units are the most commonly used atomic elements to represent speech signals in modern ASRs.However they are not the optimal choice due to several reasons such as: large amount of effort required to handcraft a pronunciation dictionary, pronunciation variations, human mistakes and under-resourced dialects and languages.Here, we propose a data-driven pronunciation estimation and acoustic modeling method which only takes the orthographic transcription to jointly estimate a set of sub-word units and a reliable dictionary.Experimental results show that the proposed method which is based on semi-supervised training of a deep neural network largely outperforms phoneme based continuous speech recognition on the TIMIT dataset.

Feature subset selection, as a special case of the general subset selection problem, has been the topic of a considerable number of studies due to the growing importance of data-mining applications. In the feature subset selection problem there are two main issues that need to be addressed: (i) Finding an appropriate measure function than can be fairly fast and robustly computed for high-dimensional data. (ii) A search strategy to optimize the measure over the subset space in a reasonable amount of time. In this article mutual information between features and class labels is considered to be the measure function. Two series expansions for mutual information are proposed, and it is shown that most heuristic criteria suggested in the literature are truncated approximations of these expansions. It is well-known that searching the whole subset space is an NP-hard problem. Here, instead of the conventional sequential search algorithms, we suggest a parallel search strategy based on semidefinite programming (SDP) that can search through the subset space in polynomial time. By exploiting the similarities between the proposed algorithm and an instance of the maximum-cut problem in graph theory, the approximation ratio of this algorithm is derived and is compared with the approximation ratio of the backward elimination method. The experiments show that it can be misleading to judge the quality of a measure solely based on the classification accuracy, without taking the effect of the non-optimum search strategy into account.

Feature subset selection, as a special case of the general subset selection problem, attracted a lot of research attention due to the growing importance of data-mining applications. However, since finding the optimal subset is an NP-hard problem, very different heuristic search methods have been suggested to approximate it. Here we propose a new second-order cone programming based search strategy to efficiently solve the feature subset selection for large-scale problems. Experimentally, it is shown that its performance is almost always better than the greedy search methods especially when the features are strongly dependent.

Given K utterances of a word and a set of sub-word units one may need a generalization of the conventional one-dimensional Viterbi algorithm to jointly decode them in order to derive their underlying word model (pronunciation). This extension is called k-dimensional Viterbi. However, as the number of utterances increases, the complexity of the k-dimensional Viterbi algorithm exponentially increases causing prohibitive computational burden. Here, we propose an approximation algorithm for the k-dimensional Viterbi which efficiently uses the available utterances to estimate the pronunciation. In addition to automatic dictionary generation, it can be used in computationally expensive applications such as lexicon-free training and joint pattern alignment. Index Terms: pronunciation, joint decoding, k-dimensional, viterbi

Under real conditions, severe signal cancellation often occurs in adaptive beamformers because of reverberation, microphone transfer function mismatch or steering vector error. Therefore, usually voice activity information is necessary to pause the beamformer update during the speech activity. However, this information is not available or not sufficiently accurate in most applications. Here we propose a new algorithm in order to mitigate the signal cancellation effect. The algorithm extracts the desired source-to-microphones transfer functions from the data covariance matrix by using rough estimates of some source locations. We show that it is robust against reverberation, microphone mismatch and imprecisely estimated target direction.