Spoken content retrieval: A survey of techniques and technologies

Speech media, that is, digital audio and video containing spoken content, has blossomed in recent years. Large collections are accruing on the Internet as well as in private and enterprise settings. This growth has motivated extensive research on techniques and technologies that facilitate reliable indexing and retrieval. Spoken content retrieval (SCR) requires the combination of audio and speech processing technologies with methods from information retrieval (IR). SCR research initially investigated planned speech structured in document-like units, but has subsequently shifted focus to more informal spoken content produced spontaneously, outside of the studio and in conversational settings. This survey provides an overview of the field of SCR encompassing component technologies, the relationship of SCR to text IR and automatic speech recognition and user interaction issues. It is aimed at researchers with backgrounds in speech technology or IR who are seeking deeper insight on how these fields are integrated to support research and development, thus addressing the core challenges of SCR

A discriminative HMM/N-gram-based retrieval approach for Mandarin spoken documents

In recent years, statistical modeling approaches have steadily gained in popularity in the field of information retrieval. This article presents an HMM/N-gram-based retrieval approach for Mandarin spoken documents. The underlying characteristics and the various structures of this approach were extensively investigated and analyzed. The retrieval capabilities were verified by tests with word- and syllable-level indexing features and comparisons to the conventional vector-space model approach. To further improve the discrimination capabilities of the HMMs, both the expectation-maximization (EM) and minimum classification error (MCE) training algorithms were introduced in training. Fusion of information via indexing word- and syllable-level features was also investigated. The spoken document retrieval experiments were performed on the Topic Detection and Tracking Corpora (TDT-2 and TDT-3). Very encouraging retrieval performance was obtained

Matching Queries to Frequently Asked Questions: Search Functionality for the MRSA Web-Portal

As part of the long-term EUREGIO MRSA-net project a system was developed which enables health care workers and the general public to quickly find answers to their questions regarding the MRSA pathogen. This paper focuses on how these questions can be answered using Information Retrieval (IR) and Natural Language Processing (NLP) techniques on a Frequently-Asked-Questions-style (FAQ) database

Automatic Segmentation and Part-Of-Speech Tagging For Tibetan: A First Step Towards Machine Translation

This paper presents what we believe to be the first reported work on Tibetan machine translation (MT). Of the three conceptually distinct components of a MT system — analysis, transfer, and generation — the first phase, consisting of POS tagging has been successfully completed. The combination POS tagger / word-segmenter was manually constructed as a rule-based multi-tagger relying on the Wilson formulation of Tibetan grammar. Partial parsing was also performed in combination with POS-tag sequence disambiguation. The component was evaluated at the task of document indexing for Information Retrieval (IR). Preliminary analysis indicated slightly better (though statistically comparable) performance to n-gram based approaches at a known-item IR task. Although segmentation is application specific, error analysis placed segmentation accuracy at 99%; the accuracy of the POS tagger is also estimated at 99% based on IR error analysis and random sampling

Holistic Vocabulary Independent Spoken Term Detection

Within this thesis, we aim at designing a loosely coupled holistic system for Spoken Term Detection (STD) on heterogeneous German broadcast data in selected application scenarios. Starting from STD on the 1-best output of a word-based speech recognizer, we study the performance of several subword units for vocabulary independent STD on a linguistically and acoustically challenging German corpus. We explore the typical error sources in subword STD, and find that they differ from the error sources in word-based speech search. We select, extend and combine a set of state-of-the-art methods for error compensation in STD in order to explicitly merge the corresponding STD error spaces through anchor-based approximate lattice retrieval. Novel methods for STD result verification are proposed in order to increase retrieval precision by exploiting external knowledge at search time. Error-compensating methods for STD typically suffer from high response times on large scale databases, and we propose scalable approaches suitable for large corpora. Highest STD accuracy is obtained by combining anchor-based approximate retrieval from both syllable lattice ASR and syllabified word ASR into a hybrid STD system, and pruning the result list using external knowledge with hybrid contextual and anti-query verification.Die vorliegende Arbeit beschreibt ein lose gekoppeltes, ganzheitliches System zur Sprachsuche auf heterogenenen deutschen Sprachdaten in unterschiedlichen Anwendungsszenarien. Ausgehend von einer wortbasierten Sprachsuche auf dem Transkript eines aktuellen Wort-Erkenners werden zunächst unterschiedliche Subwort-Einheiten für die vokabularunabhängige Sprachsuche auf deutschen Daten untersucht. Auf dieser Basis werden die typischen Fehlerquellen in der Subwort-basierten Sprachsuche analysiert. Diese Fehlerquellen unterscheiden sich vom Fall der klassichen Suche im Worttranskript und müssen explizit adressiert werden. Die explizite Kompensation der unterschiedlichen Fehlerquellen erfolgt durch einen neuartigen hybriden Ansatz zur effizienten Ankerbasierten unscharfen Wortgraph-Suche. Darüber hinaus werden neuartige Methoden zur Verifikation von Suchergebnissen vorgestellt, die zur Suchzeit verfügbares externes Wissen einbeziehen. Alle vorgestellten Verfahren werden auf einem umfangreichen Satz von deutschen Fernsehdaten mit Fokus auf ausgewählte, repräsentative Einsatzszenarien evaluiert. Da Methoden zur Fehlerkompensation in der Sprachsuchforschung typischerweise zu hohen Laufzeiten bei der Suche in großen Archiven führen, werden insbesondere auch Szenarien mit sehr großen Datenmengen betrachtet. Die höchste Suchleistung für Archive mittlerer Größe wird durch eine unscharfe und Anker-basierte Suche auf einem hybriden Index aus Silben-Wortgraphen und silbifizierter Wort-Erkennung erreicht, bei der die Suchergebnisse mit hybrider Verifikation bereinigt werden

Automatic tagging and geotagging in video collections and communities

Automatically generated tags and geotags hold great promise to improve access to video collections and online communi- ties. We overview three tasks offered in the MediaEval 2010 benchmarking initiative, for each, describing its use scenario, definition and the data set released. For each task, a reference algorithm is presented that was used within MediaEval 2010 and comments are included on lessons learned. The Tagging Task, Professional involves automatically matching episodes in a collection of Dutch television with subject labels drawn from the keyword thesaurus used by the archive staff. The Tagging Task, Wild Wild Web involves automatically predicting the tags that are assigned by users to their online videos. Finally, the Placing Task requires automatically assigning geo-coordinates to videos. The specification of each task admits the use of the full range of available information including user-generated metadata, speech recognition transcripts, audio, and visual features

A stemming algorithm for Latvian

The thesis covers construction, application and evaluation of a stemming algorithm for advanced information searching and retrieval in Latvian databases. Its aim is to examine the following two questions: Is it possible to apply for Latvian a suffix removal algorithm originally designed for English? Can stemming in Latvian produce the same or better information retrieval results than manual truncation? In order to achieve these aims, the role and importance of automatic word conflation both for document indexing and information retrieval are characterised. A review of literature, which analyzes and evaluates different types of stemming techniques and retrospective development of stemming algorithms, justifies the necessity to apply this advanced IR method also for Latvian. Comparative analysis of morphological structure both for English and Latvian language determined the selection of Porter's suffix removal algorithm as a basis for the Latvian sternmer. An extensive list of Latvian stopwords including conjunctions, particles and adverbs, was designed and added to the initial sternmer in order to eliminate insignificant words from further processing. A number of specific modifications and changes related to the Latvian language were carried out to the structure and rules of the original stemming algorithm. Analysis of word stemming based on Latvian electronic dictionary and Latvian text fragments confirmed that the suffix removal technique can be successfully applied also to Latvian language. An evaluation study of user search statements revealed that the stemming algorithm to a certain extent can improve effectiveness of information retrieval