Evaluation of noisy transcripts for spoken document retrieval

Spoken Document Retrieval (SDR) is usually implemented by using an Information Retrieval (IR) engine on speech transcripts that are produced by an Automatic Speech Recognition (ASR) system. These transcripts generally contain a substantial amount of transcription errors (noise) and are mostly unstructured. This thesis addresses two challenges that arise when doing IR on this type of source material: i. segmentation of speech transcripts into suitable retrieval units, and ii. evaluation of the impact of transcript noise on the results of an IR task.\ud It is shown that intrinsic evaluation results in different conclusions with regard to the quality of automatic story boundaries than when (extrinsic) Mean Average Precision (MAP) is used. This indicates that for automatic story segmentation for search applications, the traditionally used (intrinsic) segmentation cost may not be a good performance target. The best performance in an SDR context was achieved using lexical cohesion-based approaches, rather than the statistical approaches that were most popular in story segmentation benchmarks.\ud For the evaluation of speech transcript noise in an SDR context a novel framework is introduced, in which evaluation is done in an extrinsic, and query-dependent manner but without depending on relevance judgments. This is achieved by making a direct comparison between the ranked results lists of IR tasks on a reference and an ASR-derived transcription. The resulting measures are highly correlated with MAP, making it possible to do extrinsic evaluation of ASR transcripts for ad-hoc collections, while using a similar amount of reference material as the popular intrinsic metric Word Error Rate.\ud The proposed evaluation methods are expected to be helpful for the task of optimizing the configuration of ASR systems for the transcription of (large) speech collections for use in Spoken Document Retrieval, rather than the more traditional dictation tasks

DCU search runs at MediaEval 2012: search and hyperlinking task

We describe the runs for our participation in the Search sub-task of the Search and Hyperlinking Task at MediaEval 2012. Our runs are designed to form a retrieval baseline by using time-based segmentation of audio transcripts incorporating pause information and a sliding window to define the retrieval segments boundaries with a standard language modelling information retrieval strategy. Using this baseline system runs based on transcripts provided by LIUM were better for all evaluation metrics, than those using transcripts provided by LIMSI

Dublin City University video track experiments for TREC 2002

Dublin City University participated in the Feature Extraction task and the Search task of the TREC-2002 Video Track. In the Feature Extraction task, we submitted 3 features: Face, Speech, and Music. In the Search task, we developed an interactive video retrieval system, which incorporated the 40 hours of the video search test collection and supported user searching using our own feature extraction data along with the donated feature data and ASR transcript from other Video Track groups. This video retrieval system allows a user to specify a query based on the 10 features and ASR transcript, and the query result is a ranked list of videos that can be further browsed at the shot level. To evaluate the usefulness of the feature-based query, we have developed a second system interface that provides only ASR transcript-based querying, and we conducted an experiment with 12 test users to compare these 2 systems. Results were submitted to NIST and we are currently conducting further analysis of user performance with these 2 systems

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

Automated speech and audio analysis for semantic access to multimedia

The deployment and integration of audio processing tools can enhance the semantic annotation of multimedia content, and as a consequence, improve the effectiveness of conceptual access tools. This paper overviews the various ways in which automatic speech and audio analysis can contribute to increased granularity of automatically extracted metadata. A number of techniques will be presented, including the alignment of speech and text resources, large vocabulary speech recognition, key word spotting and speaker classification. The applicability of techniques will be discussed from a media crossing perspective. The added value of the techniques and their potential contribution to the content value chain will be illustrated by the description of two (complementary) demonstrators for browsing broadcast news archives

Overview of the CLEF-2005 cross-language speech retrieval track

The task for the CLEF-2005 cross-language speech retrieval track was to identify topically coherent segments of English interviews in a known-boundary condition. Seven teams participated, performing both monolingual and cross-language searches of ASR transcripts, automatically generated metadata, and manually generated metadata. Results indicate that monolingual search technology is sufficiently accurate to be useful for some purposes (the best mean average precision was 0.18) and cross-language searching yielded results typical of those seen in other applications (with the best systems approximating monolingual mean average precision)

Overview of VideoCLEF 2008: Automatic generation of topic-based feeds for dual language audio-visual content

The VideoCLEF track, introduced in 2008, aims to develop and evaluate tasks related to analysis of and access to multilingual multimedia content. In its first year, VideoCLEF piloted the Vid2RSS task, whose main subtask was the classification of dual language video (Dutchlanguage television content featuring English-speaking experts and studio guests). The task offered two additional discretionary subtasks: feed translation and automatic keyframe extraction. Task participants were supplied with Dutch archival metadata, Dutch speech transcripts, English speech transcripts and 10 thematic category labels, which they were required to assign to the test set videos. The videos were grouped by class label into topic-based RSS-feeds, displaying title, description and keyframe for each video. Five groups participated in the 2008 VideoCLEF track. Participants were required to collect their own training data; both Wikipedia and general web content were used. Groups deployed various classifiers (SVM, Naive Bayes and k-NN) or treated the problem as an information retrieval task. Both the Dutch speech transcripts and the archival metadata performed well as sources of indexing features, but no group succeeded in exploiting combinations of feature sources to significantly enhance performance. A small scale fluency/adequacy evaluation of the translation task output revealed the translation to be of sufficient quality to make it valuable to a non-Dutch speaking English speaker. For keyframe extraction, the strategy chosen was to select the keyframe from the shot with the most representative speech transcript content. The automatically selected shots were shown, with a small user study, to be competitive with manually selected shots. Future years of VideoCLEF will aim to expand the corpus and the class label list, as well as to extend the track to additional tasks

Multimedia retrieval in MultiMatch: The impact of speech transcript errors on search behaviour

This study discusses the findings of an evaluation study on the performance of a multimedia multimodal information access sub-system (MIAS), incorporating automatic speech recognition technology (ASR) to automatically transcribe the speech content of video soundtracks. The study’s results indicate that an information-rich but minimalist graphical interface is preferred. It was also discovered that users tend to have a misplaced confidence in the accuracy of ASR-generated speech transcripts, thus they are not inclined to conduct a systematic auditory inspection (their usual search behaviour) of a video’s soundtrack if the query term does not appear in the transcript. In order to alert the user to the possibility that a search term may be incorrectly recognised as some other word, a matching algorithm is proposed that searches for word sequences of similar phonemic structure to the query term