O papel da pausa na segmentação prosódica de corpora de fala

Raso, Tommaso; Mittmann, Maryualê Malvessi; Mendes, Anna Carolina Oliveira

doi:10.17851/2237-2083.23.3.883-922

Cited by 9 publications

(5 citation statements)

References 5 publications

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“…This list of acoustic parameters related to boundary marking is supported by a great number of experimental research on various languages, such as English (COLE; SHATTUCK-HUFNAGEL; MO, 2010; MO; COLE; LEE, 2008)54 excerpts, 11-55-s duration each, German (BATLINER et al, 1995;FUCHS et al, 2010;WESENER, 2001), Dutch (BLAAUW, 1994;SWERTS, 1997;SWERTS;COLLIER;TERKEN, 1994), Portuguese MITTMANN;MENDES, 2015)showing the interdependence between f0 and syllable-sized duration contours, showing the separate contributions of duration and f0 at minor prosodic boundaries, presenting a semi-automatic method for analysing the correlation between f0 and normalised syllablesized duration contours. Contrary to the observations in lab speech for isolated utterances, pitch accents are relatively frequent in BP (from 54 to 73 %of the phonological words and Mandarin (FON;JOHNSON;CHEN, 2011;TSENG;CHANG, 2008;TSENG et al, 2005) syllable duration, pause duration, and syllable onset intervals (SOIs, just to cite a few.…”

Section: Speech Segmentation Based On Prosodic Cuesmentioning

confidence: 91%

Acoustic Models for the Automatic Identification of Prosodic Boundaries in Spontaneous Speech / Modelos acústicos para a identificação automática de fronteiras prosódicas na fala espontânea

Teixeira

Mittmann

2018

Rev. Est. Ling.

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This work presents the results of the analysis of multiple acoustic parameters for the construction of a model for the automatic segmentation of speech in tone units. Based on literature review, we defined sets of acoustic parameters related to the signalization of terminal and non-terminal boundaries. For each parameter, we extracted a series of measurements: 6 for speech rate and rhythm; 34 for duration; 65 for fundamental frequency; 4 for intensity and 2 measurements related to pause. These parameters were extracted from spontaneous speech fragments that were previously segmented into tone units, manually performed by 14 human annotators. We used two methods of statistical classification, Random Forest (RF) and Linear Discriminant Analysis (LDA), to generate models for the identification of prosodic boundaries. After several phases of training and testing, both methods were relatively successful in identifying terminal and non-terminal boundaries. The LDA method presented a higher accuracy in the prediction of terminal and non-terminal boundaries than the RF method, therefore the model obtained with LDA was further refined. As a result, the terminal boundary model is based on 20 acoustic measurements and shows a convergence of 80% in relation to boundaries identified by annotators in the speech sample. For non-terminal boundaries, we arrived at three models that, combined, presented a convergence of 98% in relation to the boundaries identified by annotators in the sample.Keywords: speech segmentation; prosodic boundaries; spontaneous speech.Resumo: Este trabalho apresenta os resultados da análise de múltiplos parâmetros acústicos para a construção de um modelo para a segmentação automática da fala em unidades tonais. A partir da investigação da literatura, definimos conjuntos de parâmetros acústicos relacionados à identificação de fronteiras terminais e não terminais. Para cada parâmetro, uma série de medidas foram extraídas: 6 medidas de taxa de elocução e ritmo; 34 de duração; 65 de frequência fundamental; 4 de intensidade e 2 medidas relativas às pausas. Tais parâmetros foram extraídos de fragmentos de fala espontânea previamente segmentada em unidades tonais de forma manual por 14 anotadores humanos. Utilizamos dois métodos de classificação estatística, Random Forest (RF) e Linear Discriminant Analysis (LDA), para gerar modelos de identificação de fronteiras prosódicas. Após diversas fases de treinamentos e testes, ambos os métodos apresentaram sucesso relativo na identificação de fronteiras terminais e não-terminais. O método LDA apresentou maior índice de acerto na previsão de fronteiras terminais e não-terminais do que o RF, portanto, o modelo obtido com este método foi refinado. Como resultado, O modelo para as fronteiras terminais baseia-se em 20 medidas acústicas e apresenta uma convergência de 80% em relação às fronteiras identificadas pelos anotadores na amostra de fala. Para as fronteiras não terminais, chegamos a três modelos que, combinados, apresentaram uma convergência de 98% em relação às fronteiras identificadas pelos anotadores na amostra.Palavras-chave: segmentação da fala; fronteiras prosódicas; fala espontânea.

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Section: Speech Segmentation Based On Prosodic Cuesmentioning

confidence: 91%

Acoustic Models for the Automatic Identification of Prosodic Boundaries in Spontaneous Speech / Modelos acústicos para a identificação automática de fronteiras prosódicas na fala espontânea

Teixeira

Mittmann

2018

Rev. Est. Ling.

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“…Silent pauses may or may not occur at a perceived boundary. There is an estimate of 33% of utterance boundaries and 62% of tonal unit boundaries that do not coincide with a silent pause in the C-ORAL-BRASIL corpus (Raso et al 2015). Silent pauses and syllabic lengthening are also not good predictors of the boundary type, since they can cooccur with terminal and non-terminal boundaries alike, as exemplified in (3-6).…”

Section: Elementary Linguistic Units For Spoken Communicationmentioning

confidence: 99%

An automatic speech segmentation tool based on multiple acoustic parameters

Mittmann

Barbosa

2022

CHIMERA

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Speech segmentation is required not only for linguistic research based on oral corpora, but had become essential for natural language processing. Many researchers have developed different approaches to deal with the need of automatic segmentation of speech data. In this paper we discuss some of the prosodic parameters used as cues for boundary identification and present an ongoing project for the automatic segmentation of spontaneous speech developed for Brazilian Portuguese.

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“…It is known, in fact, that silent pauses, while always being seen as a sufficient condition for a boundary, do not seem a relevant one to distinguish between the two different kinds of boundaries we were looking for. Pauses can mark both TB and NTB, and, according to research conducted on spontaneous speech corpus data (Raso et al, 2015), its duration cannot be seen as a strong correlate of the nature of the boundary. The TB-nb model is made up by 20 different…”

Section: Models Built With Non-balanced Datamentioning

confidence: 99%

Modelling automatic detection of prosodic boundaries for brazilian portuguese spontaneous speech

2020

Self Cite

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Speech is segmented into intonational units marked by prosodic boundaries. This segmentation is claimed to have important consequences on syntax, information structure and cognition. This work aims both to investigate the phonetic-acoustic parameters that guide the production and perception of prosodic boundaries, and to develop models for automatic detection of prosodic boundaries in male monological spontaneous speech of Brazilian Portuguese. Two samples were segmented into intonational units by two groups of trained annotators. The boundaries perceived by the annotators were tagged as either terminal or non-terminal. A script was used to extract 111 phonetic-acoustic parameters along speech signal in a right and left windows around the boundary of each phonological word. The extracted parameters comprise measures of (1) Speech rate and rhythm; (2) Standardized segment duration; (3) Fundamental frequency; (4) Intensity; (5) Silent pause. The script considers as prosodic boundary positions at which at least 50% of the annotators indicated a boundary of the same type. A training of models composed by the parameters extracted by the script was developed; these models, were then improved heuristically. The models were developed from the two samples and from the whole data, both using non-balanced and balanced data. Linear Discriminant Analysis algorithm was adopted to produce the models. The models for terminal boundaries show a much higher performance than those for non-terminal ones. In this paper we: (i) show the methodological procedures; (ii) analyze the different models; (iii) discuss some strategies that could lead to an improvement of our results.

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O papel da pausa na segmentação prosódica de corpora de fala

Cited by 9 publications

References 5 publications

Acoustic Models for the Automatic Identification of Prosodic Boundaries in Spontaneous Speech / Modelos acústicos para a identificação automática de fronteiras prosódicas na fala espontânea

Acoustic Models for the Automatic Identification of Prosodic Boundaries in Spontaneous Speech / Modelos acústicos para a identificação automática de fronteiras prosódicas na fala espontânea

An automatic speech segmentation tool based on multiple acoustic parameters

Modelling automatic detection of prosodic boundaries for brazilian portuguese spontaneous speech

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