LREC 2008 Ingunn Amdal, Ole Morten Strand, Jørn Almberg, and Torbjørn Svendsen

1. RUNDKAST:An Annotated NorwegianBroadcast News Speech Corpus LREC 2008 Ingunn Amdal, Ole Morten Strand, Jørn Almberg, and Torbjørn Svendsen

2. Overview • Purpose of Rundkast • An overview of the database Rundkast • Structure of annotation • Orthographic transcription • Broad phonetic annotation

3. Purpose of Rundkast Databases of broadcast news can be used for a number of research topics in speech technology such as: • Supplement to existing databases of read speech for training and testing automatic speech recognition and speaker adaptation. • Research on recognition of spontaneous speech. • Research on automatic indexing of audio data. • Research on topic and/or speaker segmentation. • Research on speech/non-speech detection (e.g. background music). • International research cooperation involving speech technology for broadcast news applications. A corpus of this kind is necessary for language technology research, buthas not been available for Norwegian

4. Overview of Rundkasthttp://www.iet.ntnu.no/projects/rundkast/ Database of 77 hours radio broadcast news fromthe Norwegian Broadcasting Corporation (NRK): • Read and spontaneous speech, as well as spontaneous dialogsand multipart discussions • There is large variation between speakers, speaking styles and topics • Speaker turns may be rapid and several speakers may talk simultaneously • The quality of the recordings include studio and telephone(mobile, satellite etc) • Frequent occurrences of background noise, jingles,music and audio illustrations Funded by the Norwegian University of Science and Technology (NTNU)

5. Structure of annotation Rundkast is hierarchically organizedand orthographically annotated: • Name of programme, type and date • Name of speaker (if known) and dialect (5 regions) • Type of speech: spontaneity, channel, recording quality • Segmented in speaker turns of app. 2-5 seconds • Orthographic transcription (standard Norwegian) • Labels for noise (speaker noise, background noise etc.) • Labels for pronunciation mistakes, foreign words, unintelligible speech etc. • ~70 hrs work per hour of recording Transcriber used for annotation: ”standard”-tool

6. no speaker [i] blah blah ... more blah ... speaker 1 speaker 1 speaker 2 nontrans report [b-]noisy blah[-b] ... report filler [lp] annotation level: one episode file 1 • • • 2 • • • 3 • • • Hierarchy of annotation levels levels: 1=section, 2=speaker turn, and 3=segment

7. Orthographic transcription • The lowest level in the annotation hierarchy, segments, are transcribed orthographically. • Orthographic transcription of spoken language is a challenge, especially for Norwegian. Using dialect also in official circumstances is more and more accepted. • The majority of RUNDKAST is not compliant to any standard pronunciation. • The aim of the conventions for the orthographic transcription in RUNDKAST is to minimize uncertainty about pronunciations and facilitate consistency.

8. Orthographic transcription:Main conventions • Words are transcribed with the written forms closest to actual pronunciations. A limited number of interjections are allowed. • Text codes are used to mark mispronunciations, truncations, and unknown words. • Numbers and symbols are written out as words. • Abbreviations are not used. • Punctuation marks are restricted to comma, period, and question mark. • Space is used between spelled letters, also when acronyms have spelled pronunciation. • Capital letters are used in proper names, spellings, and acronyms, but not at the start of sentences.

9. Example annotation in Transcriber

10. Broad phonetic annotation • Part of the data were to be phonetically annotated • Use for low-level experiments in ASR (new methods), smaller Norwegian counterpart to TIMIT • Auto-segmentation for e.g. unit selection TTS • Annotation to be based on existing standards– with necessary adjustments • Exploit experience and specifications from development of Norwegian speech synthesis databases • ”Suitable” level of detail: Acoustic boundaries should be labeled, but more phonemic than phonetic • Consistency of utmost importance!

11. Broad phonetic annotation:Selected data • 10 speakers (5 male and 5 female) • Amount of speech per speaker: • app 5 min ”planned” speech and 1 min spontaneous speech • discard noisy parts (as far as possible) • from more than one programme • use turn segmentation from orthographic annotation • All in all 1 hour of speech • Approximately 1000 hours of work

12. Broad phonetic annotation:Main principles • The annotation is mainly phonemic using the phoneme symbols closest to the perceived sound • Acoustic boundaries should be marked; some acoustically motivated symbols are included • A transcription as close as possible to the citation form is preferred • Norwegian standard SAMPA is preferred • Some English phonemes included as well as dialect variants • Example: 3 variants of the /r/-sound/r/ (tap/trill)/R/ (uvular fricative)/r\/ (approximant)

13. Broad phonetic annotation:Annotation procedure • Conversion of orthographic transcription to a format suitable for automatic transcription. • Automatic segmentation with a phonotypical transcription using a speech recognizer. • Manual correction of both segments and labels by four phonetics students using Praat. • Format check. • Control of all annotation by one supervisor.

14. Broad phonetic annotation:Comments on deviations Always cases of uncertainty, need a log for these. Problem: will the log be read? Solution: Codes for deviations! • Additional Praat tier for deviations • Synchronous with the phoneme tier • Easy to utilize automatically • Examples: • creaky voice • unexpected voiced/unvoiced • uncertain boundary or symbol • ... in addition a log file with whatever deviations left

15. Example annotation in Praat

16. Concluding remarks • Availability: • Planned to be included for non-commercial use in a future Norwegian language bank • Will complement other corpora also intended to be included • To be validated by Spex • Planned use at NTNU: SIRKUS project • Investigation in new paradigms for ASR • Low-level phone recognition experiments initially • multi-linguality aspects • Spoken information retrieval