2005

This paper presents a follow up of a study on the automatic detection of prosodic prominence in continuous speech. Prosodic prominence involves two different prosodic features, pitch accent and stress, that are typically based on four acoustic parameters: fundamental frequency (F0) movements, overall syllable energy, syllable nuclei duration and mid-tohigh-frequency emphasis. 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. This automatic system has been used to cast light on the actual correlation among the acoustic parameters and the prominence phenomenon from an typological point of view, by examining data derived from some stress-accented languages.

The point of interest in the present investigation is to find out and to make a pilot statistical presentation of the prominence distinguished by native speakers in read aloud texts taken from the Russian corpus for text-to-speech unit-selection synthesis.

2006

This paper presents a follow up of a study on the automatic detection of prosodic prominence in spontaneous speech. Prosodic prominence involves two different prosodic features, pitch accent and stress, that are typically based on four acoustic parameters: fundamental frequency (F0) movements, overall syllable energy, syllable nuclei duration and mid-tohigh-frequency emphasis. A careful measurement of these acoustic 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 even when tested on spontaneous speech.

Seventh International Symposium on Signal Processing and Its Applications, 2003. Proceedings., 2003

This paper presents work in progress on the automatic detection of prosodic prominence in continuous speech. Prosodic prominence involves two different phonetic features: pitch accents, connected with fundamental frequency (F0) movements and syllable overall energy, and stress, which exhibits a strong correlation with syllable nuclei duration and high-frequency emphasis. By measuring these acoustic parameters it is possible to build an automatic system capable of correctly identifying prominent syllables with an agreement with human-tagged data comparable with the inter-human agreement reported in the literature. These results were achieved without using any information apart from acoustic parameters.

Genetic Resources and Crop Evolution, 2005

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.

2002

This paper presents work in progress on the automatic detection of prosodic prominence in continuous speech. Prosodic prominence involves two different phonetic features: pitch accents, connected with fundamental frequency (F0) movements and syllable overall energy, and stress, which exhibits a strong correlation with syllable duration and high-frequency emphasis. By deriving a set of acoustic parameters it is possible to build syllable-stress detectors as well as pitch-accent detectors and combine them to build an automatic system devoted to prominence detection. Starting from a syllable-segmented utterance, the system presented here is capable of correctly identify prominent syllables with an agreement, with human-tagged data, comparable with the inter-human agreement reported in the literature.

2015

Prosodic prominence is commonly regarded as the perceptual salience of a linguistic unit relative to its environment. However, we are far from having a consensus on how it is measured subjectively and how it relates to objectively measurable acoustic events or linguistic structures such as lexical stress, prosodic focus, etc. Here we will concentrate mainly on the identification of prominence by means of acoustic parameters and automatic techniques. Considering this topic, some questions are still open in the community: (a) How can we reliably define and portray prosodic prominence? (b) What is the best prominence domain in acoustics? (c) Is prominence a continuous or a discrete phenomenon? (d) What are the acoustic parameters that support it and how can we combine them to reliably identify prominence? (e) To what extent are acoustic parameters language specific? Can we identify universals across languages? (f) What is the best paradigm for the automatic identification of prominence...

Twelfth Annual Conference of the …, 2011

This paper describes the development and evaluation of a prosody prediction module for unit selection speech synthesis that is based on the notion of perceptual prominence. We outline the design principles of the module and describe its implementation in the Bonn Open Synthesis System (BOSS). Moreover, we report results of perception experiments that have been conducted in order to evaluate prominence prediction. The paper is concluded by a general discussion of the approach and a sketch of perspectives for further work.

2003

This paper presents work in progress on the automatic detection of prosodic prominence in continuous speech. Prosodic prominence involves two different phonetic features: pitch accents, connected with fundamental frequency (F0) movements and syllable overall energy, and stress, which exhibits a strong correlation with syllable nuclei duration and mid-to-high-frequency emphasis. By measuring these acoustic parameters it is possible to build an automatic system capable of correctly identifying prominent syllables with an agreement, with human-tagged data, comparable with the inter-human agreement reported in the literature. This system does not require any training phase, additional information or annotation, it is not tailored to a specific set of data and can be easily adapted to different languages.

Laboratory Phonology, 2010

The perception of prosodic prominence in spontaneous speech is investigated through an online task of prosody transcription using untrained listeners. Prominence is indexed through a probabilistic prominence score assigned to each word based on the proportion of transcribers who perceived the word as prominent. Correlation and regression analyses between perceived prominence, acoustic measures and measures of a word's information status are conducted to test three hypotheses: (i) prominence perception is signal-driven, influenced by acoustic factors reflecting speakers' productions; (ii) perception is expectation-driven, influenced by the listener's prior experience of word frequency and repetition; (iii) any observed influence of word frequency on perceived prominence is mediated through the acoustic signal. Results show correlates of perceived prominence in acoustic measures, in word log-frequency and in the repetition index of a word, consistent with both signal-driven and expectation-driven hypotheses of prominence perception. But the acoustic correlates of perceived prominence differ somewhat from the correlates of word frequency, suggesting an independent effect of frequency on prominence perception. A speech processing account is offered as a model of s ignal-driven and expectation-driven effects on prominence perception, where prominence ratings are a function of the ease of lexical processing, as measured through the activation levels of lexical and sub-lexical units.