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

Beyond English text: Multilingual and multimedia information retrieval.

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Phonetic Searching

An improved method and apparatus is disclosed which uses probabilistic techniques to map an input search string with a prestored audio file, and recognize certain portions of a search string phonetically. An improved interface is disclosed which permits users to input search strings, linguistics, phonetics, or a combination of both, and also allows logic functions to be specified by indicating how far separated specific phonemes are in time.Georgia Tech Research Corporatio

Multimedia search without visual analysis: the value of linguistic and contextual information

This paper addresses the focus of this special issue by analyzing the potential contribution of linguistic content and other non-image aspects to the processing of audiovisual data. It summarizes the various ways in which linguistic content analysis contributes to enhancing the semantic annotation of multimedia content, and, as a consequence, to improving the effectiveness of conceptual media access tools. A number of techniques are presented, including the time-alignment of textual resources, audio and speech processing, content reduction and reasoning tools, and the exploitation of surface features

Using term clouds to represent segment-level semantic content of podcasts

Spoken audio, like any time-continuous medium, is notoriously difficult to browse or skim without support of an interface providing semantically annotated jump points to signal the user where to listen in. Creation of time-aligned metadata by human annotators is prohibitively expensive, motivating the investigation of representations of segment-level semantic content based on transcripts generated by automatic speech recognition (ASR). This paper examines the feasibility of using term clouds to provide users with a structured representation of the semantic content of podcast episodes. Podcast episodes are visualized as a series of sub-episode segments, each represented by a term cloud derived from a transcript generated by automatic speech recognition (ASR). Quality of segment-level term clouds is measured quantitatively and their utility is investigated using a small-scale user study based on human labeled segment boundaries. Since the segment-level clouds generated from ASR-transcripts prove useful, we examine an adaptation of text tiling techniques to speech in order to be able to generate segments as part of a completely automated indexing and structuring system for browsing of spoken audio. Results demonstrate that the segments generated are comparable with human selected segment boundaries

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

Examining the contributions of automatic speech transcriptions and metadata sources for searching spontaneous conversational speech

The searching spontaneous speech can be enhanced by combining automatic speech transcriptions with semantically related metadata. An important question is what can be expected from search of such transcriptions and different sources of related metadata in terms of retrieval effectiveness. The Cross-Language Speech Retrieval (CL-SR) track at recent CLEF workshops provides a spontaneous speech test collection with manual and automatically derived metadata fields. Using this collection we investigate the comparative search effectiveness of individual fields comprising automated transcriptions and the available metadata. A further important question is how transcriptions and metadata should be combined for the greatest benefit to search accuracy. We compare simple field merging of individual fields with the extended BM25 model for weighted field combination (BM25F). Results indicate that BM25F can produce improved search accuracy, but that it is currently important to set its parameters suitably using a suitable training set

An Illustrated Methodology for Evaluating ASR Systems

Proceeding of: 9th International Workshop on Adaptive Multimedia Retrieval (AMR 2011) Took place 2011, July, 18-19, in Barcelona, Spain. The event Web site is http://stel.ub.edu/amr2011/Automatic speech recognition technology can be integrated in an information retrieval process to allow searching on multimedia contents. But, in order to assure an adequate retrieval performance is necessary to state the quality of the recognition phase, especially in speaker-independent and domainindependent environments. This paper introduces a methodology to accomplish the evaluation of different speech recognition systems in several scenarios considering also the creation of new corpora of different types (broadcast news, interviews, etc.), especially in other languages apart from English that are not widely addressed in speech community.This work has been partially supported by the Spanish Center for Industry Technological Development (CDTI, Ministry of Industry, Tourism and Trade), through the BUSCAMEDIA Project (CEN-20091026). And also by MA2VICMR: Improving the access, analysis and visibility of the multilingual and multimedia information in web for the Region of Madrid (S2009/TIC-1542).Publicad