GlobalPhone Hausa

registration: 2013/01/09 04:47:22, last modified: 2016/04/12 08:40:16
title
  • GlobalPhone Hausa
title.abbreviation
title.alternative
creator
  • researcher Karlsruhe Institute of Technology (KIT)
subject
  • speech corpus
subject.linguisticField
  • computational_linguistics
  • phonetics
subject.monoMultilingual
  • monolingual
subject.resourceSubject
  • corpus speech corpus
description
  • Desktop/Microphone
    The GlobalPhone pronunciation dictionaries, created within the framework of the multilingual speech and language corpus GlobalPhone, were developed in collaboration with the Karlsruhe Institute of Technology (KIT).

    The GlobalPhone pronunciation dictionaries contain the pronunciations of all word forms found in the transcription data of the GlobalPhone speech & text database. The pronunciation dictionaries are currently available in 17 languages: Arabic (29230 entries/27059 words), Bulgarian (20193 entries), Croatian (23497 entries/20628 words), Czech (33049 entries/32942 words), French (36837 entries/20710 words), German (48979 entries/46035 words), Hausa (42662 entries/42079 words), Japanese (18094 entries), Polish (36484 entries), Portuguese (Brazilian) (54146 entries/54130 words), Russian (28818 entries/27667 words), Spanish (Latin American) (43264 entries/33960 words), Swedish (about 25000 entries), Turkish (31330 entries/31087 words), Vietnamese (38504 entries/29974 words), Chinese-Mandarin (73388 pronunciations), and Korean (3500 syllables).

    1) Dictionary Encoding:
    The pronunciation dictionary entries consist of full word forms and are either given in the original script of that language, mostly in UTF-8 encoding (Bulgarian, Croatian, Czech, French, Polish, Russian, Spanish, Thai) corresponding to the trl-files of the GlobalPhone transcriptions or in Romanized script (Arabic, German, Hausa, Japanese, Korean, Mandarin, Portuguese, Swedish, Turkish, Vietnamese) corresponding to the rmn-files of the GlobalPhone transcriptions, respectively. In the latter case the documentation mostly provides a mapping from the Romanized to the original script.

    2) Dictionary Phone set:
    The phone sets for each language were derived individually from the literature following best practices for automatic speech processing. Each phone set is explained and described in the documentation using the international standards of the International Phonetic Alphabet (IPA). For most languages a mapping to the language independent GlobalPhone naming conventions (indicated by “M_”) is provided for the purpose of data sharing across languages to build multilingual acoustic models.

    3) Dictionary Generation:
    Whenever the grapheme-to-phoneme relationship allowed, the dictionaries were created semi-automatically in a rule-based fashion using a set of grapheme-to-phoneme mapping rules. The number of rules highly depends on the language. After the automatic creation process, all dictionaries were manually cross-checked by native speakers, correcting potential errors of the automatic pronunciation generation process. Most of the dictionaries have been applied to large vocabulary speech recognition. In many cases the GlobalPhone dictionaries were compared to straight-forward grapheme-based speech recognition and to alternative sources, such as Wiktionary and usually demonstrated to be superior in terms of quality, coverage, and accuracy.

    4) Format:
    The format of the dictionaries is the same across languages and is straight-forward. Each line consists of one word form and its pronunciation separated by blank. The pronunciation consists of a concatenation of phone symbols separated by blanks. Both, words and their pronunciations are given in tcl-script list format, i.e. enclosed in “{}”, since phones can carry tags, indicating the tone and length of a vowel, or the word boundary tag “WB”, indicating the boundary of a dictionary unit. The WB tag can for example be included as a standard question in the decision tree questions for capturing crossword models in context-dependent modeling. Pronunciation variants are indicated by () with n = 2, 3, 4,… indicating the number of variants per word. The order in which variants occur in the dictionary is not necessarily related to their frequency in the corpus.
    {word} {{w WB} o r {d WB}}

    5) Documentation: The pronunciation dictionaries for each language are complemented by a documentation that describes the format of the dictionary, the phone set including its mapping to the International Phonetic Alphabet (IPA), and the frequency distribution of the phones in the dictionary. Most of the pronunciation dictionaries have been successfully applied to large vocabulary speech recognition and references to publications are given when available.
description.price *This metadata is only as a guide
description.language
  • English
description.inputDevice
  • close_talking_microphone Sennheiser 440-6
description.inputEnvironment
  • different environmental conditions
  • noisy_condition
description.speakingStyle
  • read_speech
description.speechMode
  • monologue
description.samplingRate
  • 16 kHz
description.additionalData
  • analysis_parameter Speaker information (age, gender, occupation etc.) and information about the recording setup
publisher
contributor
contributor.attribute.author.motherTongue
contributor.attribute.author.dialect
  • standard_dialect
contributor.attribute.author.level
  • professional newspaper writers
contributor.attribute.author.age
  • adult
contributor.attribute.author.gender
contributor.attribute.speaker.motherTongue
  • native Native speakers of Hausa in Cameroon
contributor.attribute.speaker.dialect
  • dialect A variety of accents: Maroua, Douala, Yaoundé, Bafoussam, Ngaoundéré, and Nigeria
contributor.attribute.speaker.level
  • amateur
contributor.attribute.speaker.age
  • adult 16 to 60 years
contributor.attribute.speaker.gender
  • male
  • female
contributor.attribute.speaker.number
  • number_of_male_speakers 33
  • number_of_female_speakers 69
  • number_of_total_speakers 102
date.created
date.issued
  • 2012-10-26
date.modified
  • 2013-11-29
type
  • Sound
  • Text
type.discourseType
  • report
type.linguisticType
  • primary_text
type.purpose
  • developing_technologies For developing continuous speech recognition systems, and language independent and language adaptive speech recognition systems
type.style
  • speech reading: newspaper texts
type.form
  • unfixed
type.sentence
  • short
  • long
type.annotation
  • annotated Markers for spontaneous effects (e.g. stuttering, false starts) and non-verbal effects (e.g. laughing), speaker information (e.g. age and gender) and information about the recording setup
type.annotationSample
type.sample
format.extent
  • Data size: approximately 2 Gb
  • Duration: 8 hrs 44 min
  • Total number of utterances: 7,895 utterances
format.medium
  • DVD
format.encoding
format.markup
format.functionality
identifier
identifier.doi
identifier.islrn
  • 727-452-225-740-0
source
language
  • Hausa
language.area
  • Africa
relation
relation.utilization *This metadata is automatically extracted
coverage.temporal
coverage.spacial
  • West Africa
rights
  • Rights available for: Research Use, Commercial Use