Prosodic and Phonetic Features for Speaking Styles Classification and Detection

Veiga, Arlindo; Celorico, Dirce; Proença, Jorge; Candeias, Sara; Perdigão, Fernando

doi:10.1007/978-3-642-35292-8_10

Cited by 5 publications

(5 citation statements)

References 12 publications

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“…We expected to observe these events, well-known as hesitations or disfluencies, as they are a characteristic of read speech [34]. However, similarly to [10], they did not have an impact on ASR performance.…”

Section: Other Characteristics Of Ep Children's Speechmentioning

confidence: 93%

Automatically Recognising European Portuguese Children’s Speech

Hämäläinen

Cho

Candeias

et al. 2014

Lecture Notes in Computer Science

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OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible. This is an author-deposited version published in : http://oatao.univ-toulouse.fr/ Eprints ID : 13045To link to this article : DOI :10.1007/978-3-319-09761-9_1 URL : http://dx.doi.org/10.1007/978-3-319-09761-9_1To cite this version : Hämäläinen, Annika and Cho, Hyongsil and Candeias, Sara and Pellegrini, Thomas and Abad, Alberto and Tjalve, Michael and Trancoso, Isabel and Dias, Michael Automatically Recognising European Portuguese Children's Speech. (2014) In: International Conference on Computational Processing of Portuguese -PROPOR 2014, 6 October 2014 -9 October 2014 Abstract. This paper reports findings from an analysis of errors made by an automatic speech recogniser trained and tested with 3-10-year-old European Portuguese children's speech. We expected and were able to identify frequent pronunciation error patterns in the children's speech. Furthermore, we were able to correlate some of these pronunciation error patterns and automatic speech recognition errors. The findings reported in this paper are of phonetic interest but will also be useful for improving the performance of automatic speech recognisers aimed at children representing the target population of the study.Keywords: Automatic speech recognition, children's speech, error analysis, European Portuguese, fricatives, pronunciation, vowel formants. IntroductionSpeech interfaces have tremendous potential in the education of children. Speech provides a natural modality for child-computer interaction and can, at its best, contribute to a fun, motivating and engaging way of learning [1]. However, it is well known that automatically recognising children's speech is a very challenging task. Recognisers trained on adult speech tend to perform substantially worse when used by children [1][2][3][4][5][6]. Moreover, word error rates (WERs) on children's speech are usually much higher than those on adult speech, even when using a recogniser trained on children's speech, and they show a gradual decrease as the children get older [1][2][3][4][5][6][7]. The difficulty of automatically recognising children's speech can be attributed to it being acoustically and linguistically very different from adult speech [1,2]. For instance, due to their smaller vocal tracts, the fundamental and formant frequencies of children's speech are higher [1,2,[7][8][9]. What is particularly characteristic of children's speech is its higher variability as compared with adult speech, both within and across speakers [1,2]. This variability is caused by rapid developmental changes in their anatomy, speech production etc., and manifests itself, for example, in speech rate, in the degree of spontaneity, in the frequency of disfluencies, in the values of fundamental and formant frequencies, as well as in pronunciation quality [1,2,[7][8][9][10][11]. The highly variable values of acoustic parameters converge to adult levels at around 13-15 years of ...

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Section: Other Characteristics Of Ep Children's Speechmentioning

confidence: 93%

Automatically Recognising European Portuguese Children’s Speech

Hämäläinen

Cho

Candeias

et al. 2014

Lecture Notes in Computer Science

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“…Furthermore, the defined false start type (PRE) is the most common disfluency for sentences, whereas in pseudowords mispronunciations are more common since there are fewer attempts to correct unknown words. Unexpectedly, children did not use filled pauses when trying to read aloud as teen and adults do in spontaneous speech [24], and instead pause with silence while thinking about how to read.…”

Section: Corpus and Disfluenciesmentioning

confidence: 96%

Design and Analysis of a Database to Evaluate Children’s Reading Aloud Performance

Proença

Celorico

Lopes

et al. 2016

Lecture Notes in Computer Science

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To evaluate the reading performance of children, human assessment is usually involved, where a teacher or tutor has to take time to individually estimate the performance in terms of fluency (speed, accuracy and expression). Automatic estimation of reading ability can be an important alternative or complement to the usual methods, and can improve other applications such as elearning. Techniques must be developed to analyse audio recordings of read utterances by children and detect the deviations from the intended correct reading i.e. disfluencies. For that goal, a database of 284 European Portuguese children from 6 to 10 years old (1st-4th grades) reading aloud amounting to 20 hours was collected in private and public Portuguese schools. This paper describes the design of the reading tasks as well as the data collection procedure. The presence of different types of disfluencies is analysed as well as reading performance compared to known curricular goals.

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“…For European Portuguese, only recently a reduced number of studies on characterizing disfluencies have been conducted. [10] analyze the acoustic characteristics of filled pauses vs. segmental prolongations in a corpus of Portuguese broadcast news, using prosodic and spectral features to discriminate between both categories. [11,12] use the same university lectures corpus subset also used in the present study and concluded that the best features to identify whether an element should be rated as fluent or disfluent are: prosodic phrasing, contour shape, and presence/absence of silent pauses.…”

Section: Related Workmentioning

confidence: 99%

Disfluency detection based on prosodic features for university lectures

Medeiros¹,

Moniz

Batista

et al. 2013

Interspeech 2013

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This paper focuses on the identification of disfluent sequences and their distinct structural regions, based on acoustic and prosodic features. Reported experiments are based on a corpus of university lectures in European Portuguese, with roughly 32h, and a relatively high percentage of disfluencies (7.6%). The set of features automatically extracted from the corpus proved to be discriminant of the regions contained in the production of a disfluency. Several machine learning methods have been applied, but the best results were achieved using Classification and Regression Trees (CART). The set of features which was most informative for cross-region identification encompasses word duration ratios, word confidence score, silent ratios, and pitch and energy slopes. Features such as the number of phones and syllables per word proved to be more useful for the identification of the interregnum, whereas energy slopes were most suited for identifying the interruption point.

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Prosodic and Phonetic Features for Speaking Styles Classification and Detection

Cited by 5 publications

References 12 publications

Automatically Recognising European Portuguese Children’s Speech

Automatically Recognising European Portuguese Children’s Speech

Design and Analysis of a Database to Evaluate Children’s Reading Aloud Performance

Disfluency detection based on prosodic features for university lectures

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