Abstract: Hidden Markov Models HMM trajectory tiling (HTT)-based approaches may be used to synthesize speech from text. In operation, a set of Hidden Markov Models (HMMs) and a set of waveform units may be obtained from a speech corpus. The set of HMMs are further refined via minimum generation error (MGE) training to generate a refined set of HMMs. Subsequently, a speech parameter trajectory may be generated by applying the refined set of HMMs to an input text. A unit lattice of candidate waveform units may be selected from the set of waveform units based at least on the speech parameter trajectory. A normalized cross-correlation (NCC)-based search on the unit lattice may be performed to obtain a minimal concatenation cost sequence of candidate waveform units, which are concatenated into a concatenated waveform sequence that is synthesized into speech.

Abstract: An exemplary method for handwritten character generation includes receiving one or more characters and, for the one or more received characters, generating handwritten characters using Hidden Markov Models trained for generating handwritten characters. In such a method the trained Hidden Markov Models can be adapted using a technique such as a maximum a posterior technique, a maximum likelihood linear regression technique or an Eigen-space technique.

Abstract: Exemplary methods, systems, and computer-readable media for developing, training and/or using models for online handwriting recognition of characters are described. An exemplary method for building a trainable radical-based HMM for use in character recognition includes defining radical nodes, where a radical node represents a structural element of an character, and defining connection nodes, where a connection node represents a spatial relationship between two or more radicals. Such a method may include determining a number of paths in the radical-based HMM using subsequence direction histogram vector (SDHV) clustering and determining a number of states in the radical-based HMM using curvature scale space-based (CSS) corner detection.

Abstract: Exemplary techniques are described for selecting radical sets for use in probabilistic East Asian character recognition algorithms. An exemplary technique includes applying a decomposition rule to each East Asian character of the set to generate a progressive splitting graph where the progressive splitting graph comprises radicals as nodes, formulating an optimization problem to find an optimal set of radicals to represent the set of East Asian characters using maximum likelihood and minimum description length and solving the optimization problem for the optimal set of radicals. Another exemplary technique includes selecting an optimal set of radicals by using a general function that characterizes a radical with respect to other East Asian characters and a complex function that characterizes complexity of a radical.

Abstract: An exemplary method for handwritten character generation includes receiving one or more characters and, for the one or more received characters, generating handwritten characters using Hidden Markov Models trained for generating handwritten characters. In such a method the trained Hidden Markov Models can be adapted using a technique such as a maximum a posterior technique, a maximum likelihood linear regression technique or an Eigen-space technique.

Abstract: Exemplary methods, systems, and computer-readable media for developing, training and/or using models for online handwriting recognition of characters are described. An exemplary method for building a trainable radical-based HMM for use in character recognition includes defining radical nodes, where a radical node represents a structural element of an character, and defining connection nodes, where a connection node represents a spatial relationship between two or more radicals. Such a method may include determining a number of paths in the radical-based HMM using subsequence direction histogram vector (SDHV) clustering and determining a number of states in the radical-based HMM using curvature scale space-based (CSS) corner detection.

Abstract: Exemplary techniques are described for selecting radical sets for use in probabilistic East Asian character recognition algorithms. An exemplary technique includes applying a decomposition rule to each East Asian character of the set to generate a progressive splitting graph where the progressive splitting graph comprises radicals as nodes, formulating an optimization problem to find an optimal set of radicals to represent the set of East Asian characters using maximum likelihood and minimum description length and solving the optimization problem for the optimal set of radicals. Another exemplary technique includes selecting an optimal set of radicals by using a general function that characterizes a radical with respect to other East Asian characters and a complex function that characterizes complexity of a radical.