Search of spoken documents retrieves well recognized transcripts

This paper presents a series of analyses and experiments on spoken document retrieval systems: search engines that retrieve transcripts produced by speech recognizers. Results show that transcripts that match queries well tend to be recognized more accurately than transcripts that match a query less well. This result was described in past literature, however, no study or explanation of the effect has been provided until now. This paper provides such an analysis showing a relationship between word error rate and query length. The paper expands on past research by increasing the number of recognitions systems that are tested as well as showing the effect in an operational speech retrieval system. Potential future lines of enquiry are also described

DCU at MediaEval 2010 – Tagging task WildWildWeb

We describe our runs and results for the fixed label Wild Wild Web Tagging Task at MediaEval 2010. Our experiments indicate that including all words in the ASR transcripts of the document set results in better labeling accuracy than restricting the index to only words with recognition confidence above a fixed level. Additionally our results show that tagging accuracy can be improved by incorporating additional metadata describing the documents where it is available

Search of Spoken Documents Retrieves Well Recognized Transcripts

Abstract. This paper presents a series of analyses and experiments on spoken document retrieval systems: search engines that retrieve transcripts produced by speech recognizers. Results show that transcripts that match queries well tend to be recognized more accurately than transcripts that match a query less well. This result was described in past literature, however, no study or explanation of the effect has been provided until now. This paper provides such an analysis showing a relationship between word error rate and query length. The paper expands on past research by increasing the number of recognitions systems that are tested as well as showing the effect in an operational speech retrieval system. Potential future lines of enquiry are also described. 1

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