Automatic Speech Recognition Using LP-DCTC/DCS Analysis Followed by Morphological Filtering

Front-end feature extraction techniques have long been a critical component in Automatic Speech Recognition (ASR). Nonlinear filtering techniques are becoming increasingly important in this application, and are often better than linear filters at removing noise without distorting speech features. However, design and analysis of nonlinear filters are more difficult than for linear filters. Mathematical morphology, which creates filters based on shape and size characteristics, is a design structure for nonlinear filters. These filters are limited to minimum and maximum operations that introduce a deterministic bias into filtered signals. This work develops filtering structures based on a mathematical morphology that utilizes the bias while emphasizing spectral peaks. The combination of peak emphasis via LP analysis with morphological filtering results in more noise robust speech recognition rates. To help understand the behavior of these pre-processing techniques the deterministic and statistical properties of the morphological filters are compared to the properties of feature extraction techniques that do not employ such algorithms. The robust behavior of these algorithms for automatic speech recognition in the presence of rapidly fluctuating speech signals with additive and convolutional noise is illustrated. Examples of these nonlinear feature extraction techniques are given using the Aurora 2.0 and Aurora 3.0 databases. Features are computed using LP analysis alone to emphasize peaks, morphological filtering alone, or a combination of the two approaches. Although absolute best results are normally obtained using a combination of the two methods, morphological filtering alone is nearly as effective and much more computationally efficient

Spam Classification Using Machine Learning Techniques - Sinespam

Most e-mail readers spend a non-trivial amount of time regularly deleting junk e-mail (spam) messages, even as an expanding volume of such e-mail occupies server storage space and consumes network bandwidth. An ongoing challenge, therefore, rests within the development and refinement of automatic classifiers that can distinguish legitimate e-mail from spam. Some published studies have examined spam detectors using Naïve Bayesian approaches and large feature sets of binary attributes that determine the existence of common keywords in spam, and many commercial applications also use Naïve Bayesian techniques. Spammers recognize these attempts to prevent their messages and have developed tactics to circumvent these filters, but these evasive tactics are themselves patterns that human readers can often identify quickly. This work had the objectives of developing an alternative approach using a neural network (NN) classifier brained on a corpus of e-mail messages from several users. The features selection used in this work is one of the major improvements, because the feature set uses descriptive characteristics of words and messages similar to those that a human reader would use to identify spam, and the model to select the best feature set, was based on forward feature selection. Another objective in this work was to improve the spam detection near 95% of accuracy using Artificial Neural Networks; actually nobody has reached more than 89% of accuracy using ANN

AI Solutions for MDS: Artificial Intelligence Techniques for Misuse Detection and Localisation in Telecommunication Environments

This report considers the application of Articial Intelligence (AI) techniques to the problem of misuse detection and misuse localisation within telecommunications environments. A broad survey of techniques is provided, that covers inter alia rule based systems, model-based systems, case based reasoning, pattern matching, clustering and feature extraction, articial neural networks, genetic algorithms, arti cial immune systems, agent based systems, data mining and a variety of hybrid approaches. The report then considers the central issue of event correlation, that is at the heart of many misuse detection and localisation systems. The notion of being able to infer misuse by the correlation of individual temporally distributed events within a multiple data stream environment is explored, and a range of techniques, covering model based approaches, `programmed' AI and machine learning paradigms. It is found that, in general, correlation is best achieved via rule based approaches, but that these suffer from a number of drawbacks, such as the difculty of developing and maintaining an appropriate knowledge base, and the lack of ability to generalise from known misuses to new unseen misuses. Two distinct approaches are evident. One attempts to encode knowledge of known misuses, typically within rules, and use this to screen events. This approach cannot generally detect misuses for which it has not been programmed, i.e. it is prone to issuing false negatives. The other attempts to `learn' the features of event patterns that constitute normal behaviour, and, by observing patterns that do not match expected behaviour, detect when a misuse has occurred. This approach is prone to issuing false positives, i.e. inferring misuse from innocent patterns of behaviour that the system was not trained to recognise. Contemporary approaches are seen to favour hybridisation, often combining detection or localisation mechanisms for both abnormal and normal behaviour, the former to capture known cases of misuse, the latter to capture unknown cases. In some systems, these mechanisms even work together to update each other to increase detection rates and lower false positive rates. It is concluded that hybridisation offers the most promising future direction, but that a rule or state based component is likely to remain, being the most natural approach to the correlation of complex events. The challenge, then, is to mitigate the weaknesses of canonical programmed systems such that learning, generalisation and adaptation are more readily facilitated

Vermeidung von Repräsentationsheterogenitäten in realweltlichen Wissensgraphen

Knowledge graphs are repositories providing factual knowledge about entities. They are a great source of knowledge to support modern AI applications for Web search, question answering, digital assistants, and online shopping. The advantages of machine learning techniques and the Web's growth have led to colossal knowledge graphs with billions of facts about hundreds of millions of entities collected from a large variety of sources. While integrating independent knowledge sources promises rich information, it inherently leads to heterogeneities in representation due to a large variety of different conceptualizations. Thus, real-world knowledge graphs are threatened in their overall utility. Due to their sheer size, they are hardly manually curatable anymore. Automatic and semi-automatic methods are needed to cope with these vast knowledge repositories. We first address the general topic of representation heterogeneity by surveying the problem throughout various data-intensive fields: databases, ontologies, and knowledge graphs. Different techniques for automatically resolving heterogeneity issues are presented and discussed, while several open problems are identified. Next, we focus on entity heterogeneity. We show that automatic matching techniques may run into quality problems when working in a multi-knowledge graph scenario due to incorrect transitive identity links. We present four techniques that can be used to improve the quality of arbitrary entity matching tools significantly. Concerning relation heterogeneity, we show that synonymous relations in knowledge graphs pose several difficulties in querying. Therefore, we resolve these heterogeneities with knowledge graph embeddings and by Horn rule mining. All methods detect synonymous relations in knowledge graphs with high quality. Furthermore, we present a novel technique for avoiding heterogeneity issues at query time using implicit knowledge storage. We show that large neural language models are a valuable source of knowledge that is queried similarly to knowledge graphs already solving several heterogeneity issues internally.Wissensgraphen sind eine wichtige Datenquelle von Entitätswissen. Sie unterstützen viele moderne KI-Anwendungen. Dazu gehören unter anderem Websuche, die automatische Beantwortung von Fragen, digitale Assistenten und Online-Shopping. Neue Errungenschaften im maschinellen Lernen und das außerordentliche Wachstum des Internets haben zu riesigen Wissensgraphen geführt. Diese umfassen häufig Milliarden von Fakten über Hunderte von Millionen von Entitäten; häufig aus vielen verschiedenen Quellen. Während die Integration unabhängiger Wissensquellen zu einer großen Informationsvielfalt führen kann, führt sie inhärent zu Heterogenitäten in der Wissensrepräsentation. Diese Heterogenität in den Daten gefährdet den praktischen Nutzen der Wissensgraphen. Durch ihre Größe lassen sich die Wissensgraphen allerdings nicht mehr manuell bereinigen. Dafür werden heutzutage häufig automatische und halbautomatische Methoden benötigt. In dieser Arbeit befassen wir uns mit dem Thema Repräsentationsheterogenität. Wir klassifizieren Heterogenität entlang verschiedener Dimensionen und erläutern Heterogenitätsprobleme in Datenbanken, Ontologien und Wissensgraphen. Weiterhin geben wir einen knappen Überblick über verschiedene Techniken zur automatischen Lösung von Heterogenitätsproblemen. Im nächsten Kapitel beschäftigen wir uns mit Entitätsheterogenität. Wir zeigen Probleme auf, die in einem Multi-Wissensgraphen-Szenario aufgrund von fehlerhaften transitiven Links entstehen. Um diese Probleme zu lösen stellen wir vier Techniken vor, mit denen sich die Qualität beliebiger Entity-Alignment-Tools deutlich verbessern lässt. Wir zeigen, dass Relationsheterogenität in Wissensgraphen zu Problemen bei der Anfragenbeantwortung führen kann. Daher entwickeln wir verschiedene Methoden um synonyme Relationen zu finden. Eine der Methoden arbeitet mit hochdimensionalen Wissensgrapheinbettungen, die andere mit einem Rule Mining Ansatz. Beide Methoden können synonyme Relationen in Wissensgraphen mit hoher Qualität erkennen. Darüber hinaus stellen wir eine neuartige Technik zur Vermeidung von Heterogenitätsproblemen vor, bei der wir eine implizite Wissensrepräsentation verwenden. Wir zeigen, dass große neuronale Sprachmodelle eine wertvolle Wissensquelle sind, die ähnlich wie Wissensgraphen angefragt werden können. Im Sprachmodell selbst werden bereits viele der Heterogenitätsprobleme aufgelöst, so dass eine Anfrage heterogener Wissensgraphen möglich wird

Hi, how can I help you?: Automating enterprise IT support help desks

Question answering is one of the primary challenges of natural language understanding. In realizing such a system, providing complex long answers to questions is a challenging task as opposed to factoid answering as the former needs context disambiguation. The different methods explored in the literature can be broadly classified into three categories namely: 1) classification based, 2) knowledge graph based and 3) retrieval based. Individually, none of them address the need of an enterprise wide assistance system for an IT support and maintenance domain. In this domain the variance of answers is large ranging from factoid to structured operating procedures; the knowledge is present across heterogeneous data sources like application specific documentation, ticket management systems and any single technique for a general purpose assistance is unable to scale for such a landscape. To address this, we have built a cognitive platform with capabilities adopted for this domain. Further, we have built a general purpose question answering system leveraging the platform that can be instantiated for multiple products, technologies in the support domain. The system uses a novel hybrid answering model that orchestrates across a deep learning classifier, a knowledge graph based context disambiguation module and a sophisticated bag-of-words search system. This orchestration performs context switching for a provided question and also does a smooth hand-off of the question to a human expert if none of the automated techniques can provide a confident answer. This system has been deployed across 675 internal enterprise IT support and maintenance projects.Comment: To appear in IAAI 201