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.

Large recordings like radio broadcasts or audio books are an interesting additional resource for speech research but often have nothing but an orthographic transcription available in terms of annotation. The alignment between text and speech is an important first step for further processing. Conventionally, this is done by using automatic speech recognition (ASR) on the speech corpus and then aligning recognition result and transcription. This has the drawback that an ASR system needs to be available for the target language. In this paper, we introduce an approach based on forced alignment with hidden Markov models (HMM) normally applied only to shorter utterances. We show that by using a set of generalized phone models computed over phonetic groups, forced alignment is able to reliably align text and speech while being robust against transcription errors. In contrast to ASR methods, the alignment models can be used in a language-independent way. Index Terms: speech corpora annotation, sentence segmentation, large corpora processing

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

In this paper, we describe a prosody generation system for speech synthesis that makes direct use of syntax trees to obtain duration and pitch. Instead of transforming the tree through special rules or extracting isolated features from the tree, we make use of the tree structure itself to construct a superposi-tional model that is able to learn the relation between syntax and prosody. We implemented the system in our SVOX text-to-speech system and evaluated it against the existing rule-based system. Informal listening tests showed that structural informa-tion from the tree is carried over to the prosody. Index Terms: speech synthesis, prosody, syntax anaysis 1.

While automatic methods for phonetic segmentation of speech can help with rapid annotation of corpora, most methods rely either on manually segmented data to initially train the process or manual post-processing. This is very time-consuming and slows down porting of speech systems to new languages. In the context of prosody corpora for text-to-speech (TTS) systems, we investigated methods for fully automatic phoneme segmentation using only the corpora to be segmented and an automatically generated transcription. We present a new method that improves the performance of HMM-based segmentation by correcting the boundaries between the training stages of the phoneme models with high precision. We show that, while initially aimed at single speaker corpora, it performs equally well for multi-speaker corpora. Index Terms: speech synthesis, segmentation, boundary correction

We present a new method for the estimation of a continuous fundamental frequency (F0) contour. The algorithm implements a global optimization and yields virtually error-free F0 contours for high quality speech signals. Such F0 contours are subsequently used to extract a continuous fundamental wave. Some local properties of this wave, together with a number of other speech features allow to classify the frames of a speech signal into five classes: voiced, unvoiced, mixed, irregularly glottalized and silence. The presented F0 detection and frame classification can be applied to F0 modeling and prosodic modification of speech segments in high-quality concatenative speech synthesis. Index Terms: continuous F0 contour estimation, globally optimized F0 contour, voiced-unvoiced classification, irregular