Text-to-speech (TTS) synthesis is an active area of research to generate synthetic speech from the underlying text. Compared to English and many European languages, TTS is yet to mature in Malayalam, the principal language of the South Indian state of Kerala. A syllable has to be uttered with proper durational and prosodic characteristics to emulate natural speech. When it comes to poems in Malayalam, many of them have an inherent rhythm attached to them. In Malayalam, this property is characterized by the Vruta [ 28 ] in which the poem is written. Vruta decides the meter of narration of the poem. Therefore, it is only consequential that Vruta can give away vital cues about the durational and prosodic characteristics of the poem verses recited. This study intends to identify the features that determine the durational characteristics of a poem written in a particular Vruta and develop an algorithm to extract those features required to build a dataset to model the duration of syllable utterances for tuneful TTS in Malayalam. Poems written in three Vrutas, namely Kakali, Manjari, and Keka, are considered in this study. Nineteen extractible features from the orthographic representation of a poem are identified for this purpose. A standard dataset is built using these extracted features. Later, support vector machine and feed forward neural network based estimators are proposed to model the duration of Malayalam poem syllables for tuneful speech synthesis. The hyperparameters are optimized using the GridsearchCV algorithm from the Scikit-learn machine learning library [ 15 ].

Text-to-Speech Synthesis (TTS) is an active area of research to generate synthetic speech from underlying text. The identified syllables are uttered with proper duration and prosody characteristics to emulate natural speech. It falls under the category of Natural Language Processing (NLP), which aims to bridge the gap in communication between human and machine. So far as Western languages like English are concerned, the research to produce intelligent and natural synthetic speech has advanced considerably. But in a multilingual state like India, many regional languages viz. Malayalam is underexplored when it comes to NLP. In this article, we try to amalgamate the major research works performed in the area of TTS in English and the prominent Indian languages, with a special emphasis on the South Indian language, Malayalam. This review intends to provide right direction to the research activities in the language, in the area of TTS.

Recent years have witnessed phenomenal developments worldwide in the field of NLP . But developments in Indian regional languages are very few compared to them. This work is a step towards the construction of a target word sense disambiguation system in Malayalam, which is the regional language of the state of Kerala, India. Word Sense Disambiguation/Determination refers to the task of correctly identifying the sense of an ambiguous word from its context. This is considered an AI-Complete problem in the field of Natural Language Processing . For this purpose, an exclusive corpus of 1,147 contexts of target ambiguous words has been created, which to the best of our knowledge is the first attempt in Malayalam. This work describes how the performance of an unsupervised LDA-based approach towards WSD could be improved using semantic features like synonyms and co-occurrence information.

Handwritten character recognition is still a research challenge in OCR discipline, especially for Indian scripts. This paper deals with handwritten Malayalam, a major Indian script, where past work considered a small subset of characters only. In this paper we deal with complete set of basic characters, vowel and consonant signs and compound characters that may be present in the script. Here the recognition task is more difficult because of large numbers of classes and high interclass similarity. To tackle the problem, we proposed a two-stage approach. The first stage is a group classifier, where a group consists of similar characters and those that misclassify among themselves. In the second stage, a character assigned to a group in the first stage is classified to a particular character class. The proposed scheme is more accurate and efficient than a single stage scheme.

Retrieval of similar anatomical structures of brain MR images across patients would help the expert in diagnosis of diseases. In this paper, modified local binary pattern with ternary encoding called modified local ternary pattern (MOD-LTP) is introduced, which is more discriminant and less sensitive to noise in near-uniform regions, to locate slices belonging to the same level from the brain MR image database. The ternary encoding depends on a threshold, which is a user-specified one or calculated locally, based on the variance of the pixel intensities in each window. The variance-based local threshold makes the MOD-LTP more robust to noise and global illumination changes. The retrieval performance is shown to improve by taking region-based moment features of MOD-LTP and iteratively reweighting the moment features of MOD-LTP based on the user's feedback. The average rank obtained using iterated and weighted moment features of MOD-LTP with a local variance-based threshold, is one to two times better than rotational invariant LBP (Unay, D., Ekin, A. and Jasinschi, R.S. (2010) Local structure-based region-of-interest retrieval in brain MR images. IEEE Trans. Inf. Technol. Biomed., 14, 897–903.) in retrieving the first 10 relevant images.

The distance DG(v) of a vertex v in an undirected graph G is the sum of the distances between v and all other vertices of G. The set of vertices in G with maximum (minimum) distance is the antimedian (median) set of a graph G. It is proved that for arbitrary graphs G and J and a positive integer r > 2, there exists a connected graph H, such that G is the antimedian and J the median subgraphs of H, respectively, and that dH(G,J) = r. When both G and J are connected, G and J can in addition be made convex subgraphs of H. © 2009 Wiley Periodicals, Inc. NETWORKS, 2010

This paper describes a comprehensive statistical model for UWB propagation channels that is valid for a frequency range from 3-10 GHz. It is based on measurements and simulations in the following environments: residential indoor, office indoor, built-up outdoor, industrial indoor, farm environments, and body area networks. The model is independent of the used antennas. It includes the frequency dependence of the pathloss, as well as several generalizations of the Saleh-Valenzuela model, like mixed Poisson times of arrival and delay dependent cluster decay constants. The model can thus be used for realistic performance assessment of UWB systems. It was accepted by the IEEE 802.15.4a working group (WG) as standard model for evaluation of UWB system proposals