2005 NIST Language Recognition Evaluation

registration: 2016/01/17 10:21:16, last modified: 2016/06/16 11:13:37
title
  • 2005 NIST Language Recognition Evaluation
title.abbreviation
title.alternative
creator
  • researcher Audrey Le
  • researcher Alvin Martin
  • researcher Hannah Hadfield
  • researcher Jacques de Villiers
  • researcher John-Paul Hosom
  • researcher Jan van Santen
subject
  • speech database
subject.linguisticField
subject.monoMultilingual
  • multilingual
subject.resourceSubject
  • corpus
description
  • *Introduction*

    This file contains documentation for 2005 NIST Language Recognition Evaluation, Linguistic Data Consortium (LDC) catalog number LDC2008S05 and isbn 1-58563-477-8.

    The goal of the NIST (National Institute of Standards and Technology) Language Recognition Evaluation (LRE) is to establish the baseline of current performance capability for language recognition of conversational telephone speech and to lay the groundwork for further research efforts in the field. NIST conducted two previous evaluations in 1996 and 2003. For the 2005 LRE, the emphasis was on research directed toward a general base of technology to be ported to various language recognition tasks with minimum effort and the development of the ability to make more difficult discriminations between similar languages and dialects of the same language. That focus augmented the traditional evaluation goals, those being:

    * to drive the technology forward
    * to measure the state-of-the-art
    * to find the most promising algorithmic approaches
    The task evaluated was the detection of a given target language or dialect. From a test segment of speech and a target language or dialect, the system to be evaluated determined whether the speech was from the target language or dialect. The evaluation consisted of speech from the following languages and dialects:

    * English (American)
    * English (Indian)
    * Hindi
    * Japanese
    * Korean
    * Mandarin (Mainland)
    * Mandarin (Taiwan)
    * Spanish (Mexican)
    * Tamil
    The 2005 NIST Language Recognition Evaluation Plan, which includes a description of the evaluation tasks, is included with this release. Further information regarding this evaluation is also available at the NIST Language Recognition Evaluation website.

    *Data*

    Each speech file is one side of a "4-wire" telephone conversation represented as 8-bit 8 kHz mulaw data. There are 11,106 speech files in sphere (.sph) format for a total of 73.2 hours of speech. The speech data was compiled from LDC's CALLFRIEND corpora and from data collected by Oregon Health and Science University, Beaverton, Oregon.

    Each test segment was prepared using an automatic speech activity detection algorithm to identify areas and durations of speech. The test segments were stored in SPHERE file format, one segment per file. Unlike previous evaluations, areas of silence were not removed from the segments. Segments were chosen to contain a specified approximate duration of actual speech. Auxiliary information was included in the SPHERE headers to document the source file, start time, and duration of all excerpts that were used to construct the segment.

    The test segments contain three nominal durations of speech: 3 seconds, 10 seconds, and 30 seconds. Actual speech durations vary, but were constrained to be within the ranges of 2-4 seconds, 7-13 seconds, and 25-35 seconds, respectively. Note that this refers to duration of actual speech contained in segments as determined by the speech activity detection algorithm; signal durations in general are longer due to areas of silence in the segments. Shorter speech duration test segments are subsets of longer speech duration test segments; i.e., each 10-second test segment is a subset of a corresponding 30-second test segment, and each 3-second test segment is a subset of a corresponding 10-second segment. Performance was evaluated separately for test segments of each duration.

    NIST recommends using data from the 1996 and 2003 evaluations as development data. This data may be found in 2003 NIST Language Recognition Evaluation, LDC2006S31. Because the 1996 and 2003 evaluations did not cover Indian-accented English, this release includes a development data set of Indian-accented English.

    *Samples*

    For an example of the data in this corpus, please review the following audio samples(wav format): * 3 second
    * 10 second
    * 30 second
description.price *This metadata is only as a guide
description.language
  • English
description.inputDevice
  • fixed_line_telephone
description.inputEnvironment
description.speakingStyle
  • spontaneous_speech
description.speechMode
  • meeting_speech
description.samplingRate
  • 8 kHz
description.additionalData
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
  • dialect
  • standard_dialect
contributor.attribute.speaker.level
contributor.attribute.speaker.age
contributor.attribute.speaker.gender
  • male
  • female
contributor.attribute.speaker.number
date.created
date.issued
  • 2008-06-16
date.modified
type
  • Sound
type.discourseType
  • interactive_discourse
type.linguisticType
  • primary_text
type.purpose
  • developing_technologies To drive the technology forward, to measure the state-of-the-art and to find the most promising algorithmic approaches
type.style
  • speech
type.form
  • unfixed
type.sentence
  • long
  • short
type.annotation
type.annotationSample
type.sample
format.extent
  • Corpus size: 2097152 KB
  • 11,106 speech files in sphere (.sph) format for a total of 73.2 hours of speech
format.medium
  • Web Download
  • DVD
format.encoding
format.markup
format.functionality
identifier
identifier.doi
identifier.islrn
  • 747-471-848-124-3
source
language
  • Tamil
  • Korean
  • Japanese
  • Hindi
  • English
  • Spanish
  • Mandarin Chinese
language.area
  • America
  • Asia
  • Europe
  • Japan
relation
relation.utilization *This metadata is automatically extracted
coverage.temporal
coverage.spacial
rights