SLIM prosodic automatic tools for self-learning instruction

Delmonte, Rodolfo

doi:10.1016/s0167-6393(99)00043-6

Cited by 33 publications

(17 citation statements)

References 22 publications

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“…First proposed by Vardanian (1964) and then by Lane, Buiten (1969), who also provided a graphical display of duration and intensity, this method was effectively implemented in the 1970's and 1980's (James, 1976;De Bot, 1983;Cranen et al, 1984). Since then, intonation displays are frequently implemented in CALL programs (Chun, 1989(Chun, , 1998Anderson-Hsieh, 1992, 1994Delmonte et al, 1997;Delmonte, 2000;Kommissarchik, Kommissarchik, 2000;Hardison, 2004). An overview on feedback in Computer Assisted Pronunciation Training is given by Neri et al (2002).…”

Section: Introductionmentioning

confidence: 99%

Italian speakers learn lexical stress of German morphologically complex words

Bissiri

Pfitzinger

2009

Speech Communication

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Section: Introductionmentioning

confidence: 99%

Italian speakers learn lexical stress of German morphologically complex words

Bissiri

Pfitzinger

2009

Speech Communication

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“…In rapid/quick colloquial/familiar style of pronunciation in RP of free conversation and dialogue the effects of elision and compression of vowels and consonants can reach 83% elision at word boundary and 17% internal elision (Delmonte 2000). Here below we list some of the most interesting cases attested in our corpus:…”

Section: Phonological Rules For Phonological Phrasesmentioning

confidence: 99%

“…It has been developed partly under HyperCard ™ , and partly under MacroMedia Director ™ . However at present, the Prosodic Module interacts in real time only with HyperCard ™ (Delmonte 2000).…”

Section: Slim and Asrmentioning

confidence: 99%

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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“…Moreover, the construction of large annotated language resources, such as prosodically tagged speech corpora, shows an increasing interest both for research purposes and for language teaching (Hirst, 2001). Also teaching languages using CALL systems with software modules capable of properly managing prosodical information (Auberg et al, 1998;Nouza, 1999;Delmonte, 2000) seems to be an interesting option, especially in second language acquisition.…”

Section: Introductionmentioning

confidence: 99%

“…Other studies are focused on the building of automatic systems capable of reliably identifying either one acoustic correlate of prominence (e.g. Fach and Wokurek, 1995;Campione and Veronis, 1998) or a complete set of prosodic parameters, such as prominence, intonation, rhythm, etc., and their acoustic correlates (Bagshaw, 1993(Bagshaw, , 1994Wightman and Ostendorf, 1994;Jenkins and Scordilis, 1996;Delmonte, 2000). These studies typically rely on additional phonetic information, such as phone labelling and/or utterance transcriptions, for deriving prosodic phenomena.…”

Section: Introductionmentioning

confidence: 99%

An Automatic System for Detecting Prosodic Prominence in American English Continuous Speech

Tamburini¹,

Caini²

2005

Genet Resour Crop Evol

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Abstract. A precise identification of prosodic phenomena and the construction of tools able to properly manage such phenomena are essential steps to disambiguate the meaning of certain utterances. In particular they are useful for a wide variety of tasks: automatic recognition of spontaneous speech, automatic enhancement of speechgeneration systems, solving ambiguities in natural language interpretation, the construction of large annotated language resources, such as prosodically tagged speech corpora, and teaching languages to foreign students using Computer Aided Language Learning (CALL) systems. This paper presents a study on the automatic detection of prosodic prominence in continuous speech, with particular reference to American English, but with good prospects of application to other languages. Prosodic prominence involves two different prosodic features: pitch accent and stress accent. Pitch accent is acoustically connected with fundamental frequency (F0) movements and overall syllable energy, whereas stress exhibits a strong correlation with syllable nuclei duration and mid-to-high-frequency emphasis. This paper shows that a careful measurement of these acoustic parameters, as well as the identification of their connection to prosodic parameters, makes it possible to build an automatic system capable of identifying prominent syllables in utterances with performance comparable with the inter-human agreement reported in the literature. Two different prominence detectors were studied and developed: the first uses a training corpus to set up thresholds properly, while the second uses a pure unsupervised method. In both cases, it is worth stressing that only acoustic parameters derived directly from speech waveforms are exploited.

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SLIM prosodic automatic tools for self-learning instruction

Cited by 33 publications

References 22 publications

Italian speakers learn lexical stress of German morphologically complex words

Italian speakers learn lexical stress of German morphologically complex words

Prosodic tools for language learning

An Automatic System for Detecting Prosodic Prominence in American English Continuous Speech

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