2006 NIST Speaker Recognition Evaluation Test Set Part 2

registration: 2016/05/28 11:15:44, last modified: 2016/08/17 13:24:20
title
  • 2006 NIST Speaker Recognition Evaluation Test Set Part 2
title.abbreviation
title.alternative
creator
  • researcher NIST Multimodal Information Group
subject
  • speech database
subject.linguisticField
  • phonetics
  • phonology
subject.monoMultilingual
  • multilingual
subject.resourceSubject
  • corpus
description
  • *Introduction*

    2006 NIST Speaker Recognition Evaluation Test Set Part 2 was developed by LDC and NIST (National Institute of Standards and Technology). It contains 568 hours of conversational telephone and microphone speech in English, Arabic, Bengali, Chinese, Farsi, Hindi, Korean, Russian, Spanish, Thai and Urdu and associated English transcripts used as test data in the NIST-sponsored 2006 Speaker Recognition Evaluation (SRE).

    The ongoing series of SRE yearly evaluations conducted by NIST are intended to be of interest to researchers working on the general problem of text independent speaker recognition. To this end the evaluations are designed to be simple, to focus on core technology issues, to be fully supported and to be accessible to those wishing to participate.

    The task of the 2006 SRE evaluation was speaker detection, that is, to determine whether a specified speaker is speaking during a given segment of conversational telephone speech. The task was divided into 15 distinct and separate tests involving one of five training conditions and one of four test conditions. Further information about the test conditions and additional documentation is available at the NIST web site for the 2006 SRE and within the 2006 SRE Evaluation Plan.

    LDC previously published 2006 NIST Speaker Recognition Evaluation Training Set and 2006 NIST Speaker Recognition Evaluation Test Set Part 1.

    *Data*

    The speech data in this release was collected by LDC as part of the Mixer project, in particular Mixer Phases 1, 2 and 3. The Mixer project supports the development of robust speaker recognition technology by providing carefully collected and audited speech from a large pool of speakers recorded simultaneously across numerous microphones and in different communicative situations and/or in multiple languages. The data is mostly English speech, but includes some speech in Arabic, Bengali, Chinese, Farsi, Hindi, Korean, Russian, Spanish, Thai and Urdu.

    The telephone speech segments are multi-channel data collected simultaneously from a number of auxiliary microphones. The files are organized into four types: two-channel excerpts of approximately 10 seconds, two-channel conversations of approximately 5 minutes, summed-channel conversations also of approximately 5 minutes and a two-channel conversation with the usual telephone speech replaced by auxiliary microphone data in the putative target speaker channel. The auxiliary microphone conversations are also of approximately five minutes in length.

    The speech files are stored as 8-bit u-law speech signals in separate SPHERE files. In addition to the standard header fields, the SPHERE header for each file contains some auxiliary information such as the language of the conversation.

    English language transcripts in .ctm format were produced using an automatic speech recognition (ASR) system.

    *Samples*

    For an example of the data contained in this corpus, review this audio sample.

    *Updates*

    None at this time.
description.price *This metadata is only as a guide
description.language
  • English
description.inputDevice
  • desktop_microphone
  • lapel_microphone
  • fixed_line_telephone
  • mobile_phone
description.inputEnvironment
description.speakingStyle
  • spontaneous_speech
description.speechMode
  • dialogue
  • monologue
description.samplingRate
  • 8 kHz
description.additionalData
  • analysis_parameter English language transcripts in .ctm format
publisher
contributor
contributor.attribute.author.motherTongue
contributor.attribute.author.dialect
contributor.attribute.author.level
contributor.attribute.author.age
contributor.attribute.author.gender
contributor.attribute.speaker.motherTongue
  • native
contributor.attribute.speaker.dialect
  • standard_dialect
contributor.attribute.speaker.level
  • amateur
contributor.attribute.speaker.age
contributor.attribute.speaker.gender
  • female
  • male
contributor.attribute.speaker.number
date.created
date.issued
  • 2012-01-19
date.modified
type
  • Sound
  • Text
type.discourseType
  • interactive_discourse
type.linguisticType
  • primary_text
type.purpose
  • developing_technologies To be of interest to researchers working on the general problem of text independent speaker recognition.
type.style
  • speech
  • written
type.form
  • unfixed
type.sentence
  • long
  • short
type.annotation
type.annotationSample
type.sample
format.extent
  • Corpus size: 28289338 KB
format.medium
  • Web Download
format.encoding
format.markup
format.functionality
identifier
identifier.doi
identifier.islrn
  • 125-164-075-830-3
source
  • Mixer Phases 1, 2 and 3
language
  • Yue Chinese
  • Urdu
  • Thai
  • Spanish
  • Russian
  • Korean
  • Hindi
  • Persian
  • English
  • Mandarin Chinese
  • Bengali
  • Standard Arabic
  • Persian Dari
  • Persian Iranian Persian
  • Chinese
  • Arabic
  • Dari
language.area
  • America
  • Asia
  • Europe
relation
relation.utilization *This metadata is automatically extracted
coverage.temporal
coverage.spacial
rights