M = Syntax + Prosody: A syntactic–prosodic labelling scheme for large spontaneous speech databases

Batliner, Anton; Kompe, Ralf; Kießling, Andreas; Mast, Marion; Niemann, Heinrich; Nöth, Elmar

doi:10.1016/s0167-6393(98)00037-5

Cited by 41 publications

(48 citation statements)

References 32 publications

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“…As has been clearly shown in recent experiments (Bannert 1987;Batliner et al 1998;Breen 1995) prosodic information can be fruitfully used in speech recognition tasks, contributing higher level linguistic knowledge and improving the overall performance of the system. Stress and rhythm related phenomena seem to be explainable mainly by means of syllable level prosodic labeling.…”

Section: From Syllable Structure To Intonational Phrasementioning

confidence: 90%

Prosodic tools for language learning

Delmonte

2009

Int J Speech Technol

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In this paper we will be concerned with the role played by prosody in language learning and by the speech technology already available as commercial product or as prototype, capable to cope with the task of helping language learner in improving their knowledge of a second language from the prosodic point of view. The paper has been divided into two separate sections: Section One, dealing with Rhythm and all related topics; Section Two dealing with Intonation. In the Introduction we will argue that the use of ASR (Automatic Speech Recognition) as Teaching Aid should be under-utilized and should be targeted to narrowly focussed spoken exercises, disallowing openended dialogues, in order to ensure consistency of evaluation. Eventually, we will support the conjoined use of ASR technology and prosodic tools to produce GOP useable for linguistically consistent and adequate feedback to the student. This will be illustrated by presenting State of the Art for both sections, with systems well documented in the scientific literature of the respective field.In order to discuss the scientific foundations of prosodic analysis we will present data related to English and Italian and make comparisons to clarify the issues at hand. In this context, we will also present the Prosodic Module of a courseware for computer-assisted foreign language learning called SLIM-an acronym for Multimedia Interactive Linguistic Software, developed at the University of Venice (Delmonte et al. in Convegno GFS-AIA, pp. 47-58, 1996a; Ed-Media 96, AACE, pp. 326-333, 1996b). The Prosodic Module has been created in order to deal with the problem R. Delmonte ( ) Linguistic Computational Laboratory (LCL), Università Ca' Foscari-Ca' Bembo, Dorsoduro, 1075-30123 Venezia, Italy e-mail: delmont@unive.it url: project.cgm.unive.it of improving a student's performance both in the perception and production of prosodic aspects of spoken language activities. It is composed of two different sets of Learning Activities, the first one dealing with phonetic and prosodic problems at word level and at syllable level; the second one dealing with prosodic aspects at phonological phrase and utterance suprasegmental level. The main goal of Prosodic Activities is to ensure consistent and pedagogically sound feedback to the student intending to improve his/her pronunciation in a foreign language.

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Section: From Syllable Structure To Intonational Phrasementioning

confidence: 90%

Prosodic tools for language learning

Delmonte

2009

Int J Speech Technol

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“…Ennélfogva sokkal nagyobb mértékben képes a hangosítás jellemzőire figyelni, illetőleg azokat tudatosan megvalósítani. Megfe lelő központozással ellátott, felolvasott szövegekben a mondathatárok percepciós jelölése 90%-osnál valamivel jobb egyöntetű eredményt hozott (Batliner et al 1998).…”

Section: Bevezetésunclassified

“…Batliner et al 1998;Gallwitz et al 2002). Korpuszokon végzett vizsgálatok eredményei azt mutatták, hogy a prozódiai (és egyéb) kul csok különböznek a szöveg típusától függően.…”

Section: Következtetésekunclassified

A verbális tanári instrukciók az osztálytermi diskurzusban III.

Molnár¹

2016

Anyanyelv-pedagógia

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“…Hence, several different schemes have been proposed for annotation of typical spontaneous speech phenomena. Earliest efforts include the manual for disfluency tagging of the Switchboard corpus [7], Heeman's annotation scheme for the Trains dialog corpus [8], and a syntactic-prosodic labeling system for spontaneous speech called "M" presented in [9]. Recently, Fitzgerald and Jelinek [10] presented a new annotation scheme for spontaneous speech reconstruction.…”

Section: Structural Metadata Annotation 41 Annotation Approachmentioning

confidence: 99%

The Czech Broadcast Conversation Corpus

Kolář

Švec

2009

Text, Speech and Dialogue

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Abstract. This paper presents the final version of the Czech Broadcast Conversation Corpus that will shortly be released at the Linguistic Data Consortium (LDC). The corpus contains 72 recordings of a radio discussion program, which yields about 33 hours of transcribed conversational speech from 128 speakers. The release does not only include verbatim transcripts and speaker information, but also structural metadata (MDE) annotation that involves labeling of sentencelike unit boundaries, marking of non-content words like filled pauses and discourse markers, and annotation of speech disfluencies. The MDE annotation is based on the LDC's annotation standard for English, with changes applied to accommodate phenomena that are specific for Czech. In addition to its importance to speech recognition, speaker diarization, and structural metadata extraction research, the corpus is also useful for linguistic analysis of conversational Czech.

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M = Syntax + Prosody: A syntactic–prosodic labelling scheme for large spontaneous speech databases

Cited by 41 publications

References 32 publications

Prosodic tools for language learning

Prosodic tools for language learning

A verbális tanári instrukciók az osztálytermi diskurzusban III.

The Czech Broadcast Conversation Corpus

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