Continuous kannada speech segmentation and speech recognition based on threshold using MFCC And VQ

Continuous speech segmentation and its  recognition is playing important role in natural language processing. Continuous context based Kannada speech segmentation depends  on context, grammer and semantics rules present in the kannada language. The significant feature extraction of kannada speech signal  for recognition system is quite exciting for researchers. In this paper proposed method  is  divided into two parts. First part of the method is continuous kannada speech signal segmentation with respect to the context based is carried out  by computing  average short term energy and its spectral centroid coefficients of  the speech signal present in the specified window. The segmented outputs are completely  meaningful  segmentation  for different scenarios with less segmentation error. The second part of the method is speech recognition by extracting less number Mel frequency cepstral coefficients with less  number of codebooks  using vector quantization .In this recognition is completely based on threshold value.This threshold setting is a challenging task however the simple method is used to achieve better recognition rate.The experimental results shows more efficient  and effective segmentation    with high recognition rate for any continuous context based kannada speech signal with different accents for male and female than the existing methods and also used minimal feature dimensions for training data

Syväoppiminen puhutun kielen tunnistamisessa

This thesis applies deep learning based classification techniques to identify natural languages from speech. The primary motivation behind this thesis is to implement accurate techniques for segmenting multimedia materials by the languages spoken in them. Several existing state-of-the-art, deep learning based approaches are discussed and a subset of the discussed approaches are selected for quantitative experimentation. The selected model architectures are trained on several well-known spoken language identification datasets containing several different languages. Segmentation granularity varies between models, some supporting input audio lengths of 0.2 seconds, while others require 10 second long input to make a language decision. Results from the thesis experiments show that an unsupervised representation of acoustic units, produced by a deep sequence-to-sequence auto encoder, cannot reach the language identification performance of a supervised representation, produced by a multilingual phoneme recognizer. Contrary to most existing results, in this thesis, acoustic-phonetic language classifiers trained on labeled spectral representations outperform phonotactic classifiers trained on bottleneck features of a multilingual phoneme recognizer. More work is required, using transcribed datasets and automatic speech recognition techniques, to investigate why phoneme embeddings did not outperform simple, labeled spectral features. While an accurate online language segmentation tool for multimedia materials could not be constructed, the work completed in this thesis provides several insights for building feasible, modern spoken language identification systems. As a side-product of the experiments performed during this thesis, a free open source spoken language identification software library called "lidbox" was developed, allowing future experiments to begin where the experiments of this thesis end.Tämä diplomityö keskittyy soveltamaan syviä neuroverkkomalleja luonnollisten kielien automaattiseen tunnistamiseen puheesta. Tämän työn ensisijainen tavoite on toteuttaa tarkka menetelmä multimediamateriaalien ositteluun niissä esiintyvien puhuttujen kielien perusteella. Työssä tarkastellaan useampaa jo olemassa olevaa neuroverkkoihin perustuvaa lähestymistapaa, joista valitaan alijoukko tarkempaan tarkasteluun, kvantitatiivisten kokeiden suorittamiseksi. Valitut malliarkkitehtuurit koulutetaan käyttäen eri puhetietokantoja, sisältäen useampia eri kieliä. Kieliosittelun hienojakoisuus vaihtelee käytettyjen mallien mukaan, 0,2 sekunnista 10 sekuntiin, riippuen kuinka pitkän aikaikkunan perusteella malli pystyy tuottamaan kieliennusteen. Diplomityön aikana suoritetut kokeet osoittavat, että sekvenssiautoenkoodaajalla ohjaamattomasti löydetty puheen diskreetti akustinen esitysmuoto ei ole riittävä kielen tunnistamista varten, verrattuna foneemitunnistimen tuottamaan, ohjatusti opetettuun foneemiesitysmuotoon. Tässä työssä havaittiin, että akustisfoneettiset kielentunnistusmallit saavuttavat korkeamman kielentunnistustarkkuuden kuin foneemiesitysmuotoa käyttävät kielentunnistusmallit, mikä eroaa monista kirjallisuudessa esitetyistä tuloksista. Diplomityön tutkimuksia on jatkettava, esimerkiksi litteroituja puhetietokantoja ja puheentunnistusmenetelmiä käyttäen, jotta pystyttäisiin selittämään miksi foneemimallin tuottamalla esitysmuodolla ei saatu parempia tuloksia kuin yksinkertaisemmalla, taajuusspektrin esitysmuodolla. Tämän työn aikana puhutun kielen tunnistaminen osoittautui huomattavasti haasteellisemmaksi kuin mitä työn alussa oli arvioitu, eikä työn aikana onnistuttu toteuttamaan tarpeeksi tarkkaa multimediamateriaalien kielienosittelumenetelmää. Tästä huolimatta, työssä esitetyt lähestymistavat tarjoavat toimivia käytännön menetelmiä puhutun kielen tunnistamiseen tarkoitettujen, modernien järjestelmien rakentamiseksi. Tämän diplomityön sivutuotteena syntyi myös puhutun kielen tunnistamiseen tarkoitettu avoimen lähdekoodin kirjasto nimeltä "lidbox", jonka ansiosta tämän työn kvantitatiivisia kokeita voi jatkaa siitä, mihin ne tämän työn päätteeksi jäivät

General methods for fine-grained morphological and syntactic disambiguation

We present methods for improved handling of morphologically rich languages (MRLS) where we define MRLS as languages that are morphologically more complex than English. Standard algorithms for language modeling, tagging and parsing have problems with the productive nature of such languages. Consider for example the possible forms of a typical English verb like work that generally has four four different forms: work, works, working and worked. Its Spanish counterpart trabajar has 6 different forms in present tense: trabajo, trabajas, trabaja, trabajamos, trabajáis and trabajan and more than 50 different forms when including the different tenses, moods (indicative, subjunctive and imperative) and participles. Such a high number of forms leads to sparsity issues: In a recent Wikipedia dump of more than 400 million tokens we find that 20 of these forms occur only twice or less and that 10 forms do not occur at all. This means that even if we only need unlabeled data to estimate a model and even when looking at a relatively common and frequent verb, we do not have enough data to make reasonable estimates for some of its forms. However, if we decompose an unseen form such as trabajaréis `you will work', we find that it is trabajar in future tense and second person plural. This allows us to make the predictions that are needed to decide on the grammaticality (language modeling) or syntax (tagging and parsing) of a sentence. In the first part of this thesis, we develop a morphological language model. A language model estimates the grammaticality and coherence of a sentence. Most language models used today are word-based n-gram models, which means that they estimate the transitional probability of a word following a history, the sequence of the (n - 1) preceding words. The probabilities are estimated from the frequencies of the history and the history followed by the target word in a huge text corpus. If either of the sequences is unseen, the length of the history has to be reduced. This leads to a less accurate estimate as less context is taken into account. Our morphological language model estimates an additional probability from the morphological classes of the words. These classes are built automatically by extracting morphological features from the word forms. To this end, we use unsupervised segmentation algorithms to find the suffixes of word forms. Such an algorithm might for example segment trabajaréis into trabaja and réis and we can then estimate the properties of trabajaréis from other word forms with the same or similar morphological properties. The data-driven nature of the segmentation algorithms allows them to not only find inflectional suffixes (such as -réis), but also more derivational phenomena such as the head nouns of compounds or even endings such as -tec, which identify technology oriented companies such as Vortec, Memotec and Portec and would not be regarded as a morphological suffix by traditional linguistics. Additionally, we extract shape features such as if a form contains digits or capital characters. This is important because many rare or unseen forms are proper names or numbers and often do not have meaningful suffixes. Our class-based morphological model is then interpolated with a word-based model to combine the generalization capabilities of the first and the high accuracy in case of sufficient data of the second. We evaluate our model across 21 European languages and find improvements between 3% and 11% in perplexity, a standard language modeling evaluation measure. Improvements are highest for languages with more productive and complex morphology such as Finnish and Estonian, but also visible for languages with a relatively simple morphology such as English and Dutch. We conclude that a morphological component yields consistent improvements for all the tested languages and argue that it should be part of every language model. Dependency trees represent the syntactic structure of a sentence by attaching each word to its syntactic head, the word it is directly modifying. Dependency parsing is usually tackled using heavily lexicalized (word-based) models and a thorough morphological preprocessing is important for optimal performance, especially for MRLS. We investigate if the lack of morphological features can be compensated by features induced using hidden Markov models with latent annotations (HMM-LAs) and find this to be the case for German. HMM-LAs were proposed as a method to increase part-of-speech tagging accuracy. The model splits the observed part-of-speech tags (such as verb and noun) into subtags. An expectation maximization algorithm is then used to fit the subtags to different roles. A verb tag for example might be split into an auxiliary verb and a full verb subtag. Such a split is usually beneficial because these two verb classes have different contexts. That is, a full verb might follow an auxiliary verb, but usually not another full verb. For German and English, we find that our model leads to consistent improvements over a parser not using subtag features. Looking at the labeled attachment score (LAS), the number of words correctly attached to their head, we observe an improvement from 90.34 to 90.75 for English and from 87.92 to 88.24 for German. For German, we additionally find that our model achieves almost the same performance (88.24) as a model using tags annotated by a supervised morphological tagger (LAS of 88.35). We also find that the German latent tags correlate with morphology. Articles for example are split by their grammatical case. We also investigate the part-of-speech tagging accuracies of models using the traditional treebank tagset and models using induced tagsets of the same size and find that the latter outperform the former, but are in turn outperformed by a discriminative tagger. Furthermore, we present a method for fast and accurate morphological tagging. While part-of-speech tagging annotates tokens in context with their respective word categories, morphological tagging produces a complete annotation containing all the relevant inflectional features such as case, gender and tense. A complete reading is represented as a single tag. As a reading might consist of several morphological features the resulting tagset usually contains hundreds or even thousands of tags. This is an issue for many decoding algorithms such as Viterbi which have runtimes depending quadratically on the number of tags. In the case of morphological tagging, the problem can be avoided by using a morphological analyzer. A morphological analyzer is a manually created finite-state transducer that produces the possible morphological readings of a word form. This analyzer can be used to prune the tagging lattice and to allow for the application of standard sequence labeling algorithms. The downside of this approach is that such an analyzer is not available for every language or might not have the coverage required for the task. Additionally, the output tags of some analyzers are not compatible with the annotations of the treebanks, which might require some manual mapping of the different annotations or even to reduce the complexity of the annotation. To avoid this problem we propose to use the posterior probabilities of a conditional random field (CRF) lattice to prune the space of possible taggings. At the zero-order level the posterior probabilities of a token can be calculated independently from the other tokens of a sentence. The necessary computations can thus be performed in linear time. The features available to the model at this time are similar to the features used by a morphological analyzer (essentially the word form and features based on it), but also include the immediate lexical context. As the ambiguity of word types varies substantially, we just fix the average number of readings after pruning by dynamically estimating a probability threshold. Once we obtain the pruned lattice, we can add tag transitions and convert it into a first-order lattice. The quadratic forward-backward computations are now executed on the remaining plausible readings and thus efficient. We can now continue pruning and extending the lattice order at a relatively low additional runtime cost (depending on the pruning thresholds). The training of the model can be implemented efficiently by applying stochastic gradient descent (SGD). The CRF gradient can be calculated from a lattice of any order as long as the correct reading is still in the lattice. During training, we thus run the lattice pruning until we either reach the maximal order or until the correct reading is pruned. If the reading is pruned we perform the gradient update with the highest order lattice still containing the reading. This approach is similar to early updating in the structured perceptron literature and forces the model to learn how to keep the correct readings in the lower order lattices. In practice, we observe a high number of lower updates during the first training epoch and almost exclusively higher order updates during later epochs. We evaluate our CRF tagger on six languages with different morphological properties. We find that for languages with a high word form ambiguity such as German, the pruning results in a moderate drop in tagging accuracy while for languages with less ambiguity such as Spanish and Hungarian the loss due to pruning is negligible. However, our pruning strategy allows us to train higher order models (order > 1), which give substantial improvements for all languages and also outperform unpruned first-order models. That is, the model might lose some of the correct readings during pruning, but is also able to solve more of the harder cases that require more context. We also find our model to substantially and significantly outperform a number of frequently used taggers such as Morfette and SVMTool. Based on our morphological tagger we develop a simple method to increase the performance of a state-of-the-art constituency parser. A constituency tree describes the syntactic properties of a sentence by assigning spans of text to a hierarchical bracket structure. developed a language-independent approach for the automatic annotation of accurate and compact grammars. Their implementation -- known as the Berkeley parser -- gives state-of-the-art results for many languages such as English and German. For some MRLS such as Basque and Korean, however, the parser gives unsatisfactory results because of its simple unknown word model. This model maps unknown words to a small number of signatures (similar to our morphological classes). These signatures do not seem expressive enough for many of the subtle distinctions made during parsing. We propose to replace rare words by the morphological reading generated by our tagger instead. The motivation is twofold. First, our tagger has access to a number of lexical and sublexical features not available during parsing. Second, we expect the morphological readings to contain most of the information required to make the correct parsing decision even though we know that things such as the correct attachment of prepositional phrases might require some notion of lexical semantics. In experiments on the SPMRL 2013 dataset of nine MRLS we find our method to give improvements for all languages except French for which we observe a minor drop in the Parseval score of 0.06. For Hebrew, Hungarian and Basque we find substantial absolute improvements of 5.65, 11.87 and 15.16, respectively. We also performed an extensive evaluation on the utility of word representations for morphological tagging. Our goal was to reduce the drop in performance that is caused when a model trained on a specific domain is applied to some other domain. This problem is usually addressed by domain adaption (DA). DA adapts a model towards a specific domain using a small amount of labeled or a huge amount of unlabeled data from that domain. However, this procedure requires us to train a model for every target domain. Instead we are trying to build a robust system that is trained on domain-specific labeled and domain-independent or general unlabeled data. We believe word representations to be key in the development of such models because they allow us to leverage unlabeled data efficiently. We compare data-driven representations to manually created morphological analyzers. We understand data-driven representations as models that cluster word forms or map them to a vectorial representation. Examples heavily used in the literature include Brown clusters, Singular Value Decompositions of count vectors and neural-network-based embeddings. We create a test suite of six languages consisting of in-domain and out-of-domain test sets. To this end we converted annotations for Spanish and Czech and annotated the German part of the Smultron treebank with a morphological layer. In our experiments on these data sets we find Brown clusters to outperform the other data-driven representations. Regarding the comparison with morphological analyzers, we find Brown clusters to give slightly better performance in part-of-speech tagging, but to be substantially outperformed in morphological tagging

Word based off-line handwritten Arabic classification and recognition. Design of automatic recognition system for large vocabulary offline handwritten Arabic words using machine learning approaches.

The design of a machine which reads unconstrained words still remains an unsolved problem. For example, automatic interpretation of handwritten documents by a computer is still under research. Most systems attempt to segment words into letters and read words one character at a time. However, segmenting handwritten words is very difficult. So to avoid this words are treated as a whole. This research investigates a number of features computed from whole words for the recognition of handwritten words in particular. Arabic text classification and recognition is a complicated process compared to Latin and Chinese text recognition systems. This is due to the nature cursiveness of Arabic text. The work presented in this thesis is proposed for word based recognition of handwritten Arabic scripts. This work is divided into three main stages to provide a recognition system. The first stage is the pre-processing, which applies efficient pre-processing methods which are essential for automatic recognition of handwritten documents. In this stage, techniques for detecting baseline and segmenting words in handwritten Arabic text are presented. Then connected components are extracted, and distances between different components are analyzed. The statistical distribution of these distances is then obtained to determine an optimal threshold for word segmentation. The second stage is feature extraction. This stage makes use of the normalized images to extract features that are essential in recognizing the images. Various method of feature extraction are implemented and examined. The third and final stage is the classification. Various classifiers are used for classification such as K nearest neighbour classifier (k-NN), neural network classifier (NN), Hidden Markov models (HMMs), and the Dynamic Bayesian Network (DBN). To test this concept, the particular pattern recognition problem studied is the classification of 32492 words using ii the IFN/ENIT database. The results were promising and very encouraging in terms of improved baseline detection and word segmentation for further recognition. Moreover, several feature subsets were examined and a best recognition performance of 81.5% is achieved

Recent Trends in Computational Intelligence

Traditional models struggle to cope with complexity, noise, and the existence of a changing environment, while Computational Intelligence (CI) offers solutions to complicated problems as well as reverse problems. The main feature of CI is adaptability, spanning the fields of machine learning and computational neuroscience. CI also comprises biologically-inspired technologies such as the intellect of swarm as part of evolutionary computation and encompassing wider areas such as image processing, data collection, and natural language processing. This book aims to discuss the usage of CI for optimal solving of various applications proving its wide reach and relevance. Bounding of optimization methods and data mining strategies make a strong and reliable prediction tool for handling real-life applications