The THISL SDR system at TREC-9.

This paper describes our participation in the TREC-9 Spoken Document Retrieval (SDR) track. The THISL SDR system consists of a realtime version of a hybrid connectionist/HMM large vocabulary speech recognition system and a probabilistic text retrieval system. This paper describes the configuration of the speech recognition and text retrieval systems, including segmentation and query expansion. We report our results for development tests using the TREC-8 queries, and for the TREC-9 evaluation

Speech and hand transcribed retrieval

This paper describes the issues and preliminary work involved in the creation of an information retrieval system that will manage the retrieval from collections composed of both speech recognised and ordinary text documents. In previous work, it has been shown that because of recognition errors, ordinary documents are generally retrieved in preference to recognised ones. Means of correcting or eliminating the observed bias is the subject of this paper. Initial ideas and some preliminary results are presented

Análise comparativa dos modelos e sistemas probabilísticos em recuperação de informação em bases textuais

Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico. Programa de Pós-Graduação em Computação.A dificuldade de encontrar uma informação específica, é um dos grandes problemas encontrados hoje em dia. A Recuperação de Informação (IR) é uma área da computação que estuda o desenvolvimento de técnicas para permitir o acesso rápido a uma grande quantidade de informações. Estas informações podem ser: texto, vídeo ou áudio. Dentre os modelos clássicos de IR destacam-se três: Booleano, Vetor Espacial e Probabilístico. Neste trabalho estudar-se-ão os modelos clássicos, em especial os probabilísticos alternativos em IR. Os modelos probabilísticos são baseados no Princípio da Classificação da Probabilidade. Muitos modelos probabilísticos estão sendo estudados, mais um dos grandes problemas é trazer somente o conjunto de informações realmente importantes para a necessidade do usuário. Este trabalho descreve os modelos e sistemas probabilísticos em recuperação de informação textual, com o objetivo de analisar suas características, limitações e resultados, a fim de prover melhorias e contribuir para o aperfeiçoamento dos modelos e sistemas propostos

Information Retrieval on Noisy Text

Spoken Document Retrieval (SDR) consists in retrieving segments of a speech database that are relevant to a query. The state-of-the-art approach to the SDR problem consists in transcribing the speech data into digital text before applying common Information Retrieval (IR) techniques. The transcription, produced by an Automatic Speech Recognition system, contains recognition errors. These errors can be referred to as noise. This thesis investigates the effect of this noise on the retrieval process. We compare the results obtained with clean and noisy data at different steps of the retrieval process. To perform such a task, standard IR measures (precision, recall, break-even point, etc.) are used. It is shown that even with very different error rates (10\% vs 30\%), the performances obtained over noisy text are only slightly lower than those over clean text (9\% degradation of average precision for our complete IR system, 45.2\% vs 41.2\%)

Lattice-based statistical spoken document retrieval

Ph.DDOCTOR OF PHILOSOPH

Abberley The THISL SDR system at TREC-9

This paper describes our participation in the TREC-9 Spoken Document Retrieval (SDR) track. The THISL SDR system consists of a realtime version of a hybrid connectionist/HMM large vocabulary speech recognition system and a probabilistic text retrieval system. This paper describes the configuration of the speech recognition and text retrieval systems, including segmentation and query expansion. We report our results for development tests using the TREC-8 queries, and for the TREC-9 evaluation. 1

Spoken content retrieval beyond pipeline integration of automatic speech recognition and information retrieval

The dramatic increase in the creation of multimedia content is leading to the development of large archives in which a substantial amount of the information is in spoken form. Efficient access to this information requires effective spoken content retrieval (SCR) methods. Traditionally, SCR systems have focused on a pipeline integration of two fundamental technologies: transcription using automatic speech recognition (ASR) and search supported using text-based information retrieval (IR). Existing SCR approaches estimate the relevance of a spoken retrieval item based on the lexical overlap between a user’s query and the textual transcriptions of the items. However, the speech signal contains other potentially valuable non-lexical information that remains largely unexploited by SCR approaches. Particularly, acoustic correlates of speech prosody, that have been shown useful to identify salient words and determine topic changes, have not been exploited by existing SCR approaches. In addition, the temporal nature of multimedia content means that accessing content is a user intensive, time consuming process. In order to minimise user effort in locating relevant content, SCR systems could suggest playback points in retrieved content indicating the locations where the system believes relevant information may be found. This typically requires adopting a segmentation mechanism for splitting documents into smaller “elements” to be ranked and from which suitable playback points could be selected. Existing segmentation approaches do not generalise well to every possible information need or provide robustness to ASR errors. This thesis extends SCR beyond the standard ASR and IR pipeline approach by: (i) exploring the utilisation of prosodic information as complementary evidence of topical relevance to enhance current SCR approaches; (ii) determining elements of content that, when retrieved, minimise user search effort and provide increased robustness to ASR errors; and (iii) developing enhanced evaluation measures that could better capture the factors that affect user satisfaction in SCR