Prosodic feature extraction for regional Tamil dialects

Mohamed, S Raja; Raviraj, P.

doi:10.1109/icetect.2011.5760251

Cited by 3 publications

(2 citation statements)

References 3 publications

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“…Emotional speech recordings were collected from 30 children aged 8–12 years: 12 Russian-speaking children (born and living in St. Petersburg, Russia), 18 Tamil-speaking children (born and living in Vellore, India). The Tamil language is mainly slang influenced [ 25 ]. In this study, we have used Chennai / Vellore based common slang of the Tamil language speaking children.…”

Section: Methodsmentioning

confidence: 99%

The children’s emotional speech recognition by adults: Cross-cultural study on Russian and Tamil language

et al. 2023

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The current study investigated the features of cross-cultural recognition of four basic emotions “joy–neutral (calm state)–sad–anger” in the spontaneous and acting speech of Indian and Russian children aged 8–12 years across Russian and Tamil languages. The research tasks were to examine the ability of Russian and Indian experts to recognize the state of Russian and Indian children by their speech, determine the acoustic features of correctly recognized speech samples, and specify the influence of the expert’s language on the cross-cultural recognition of the emotional states of children. The study includes a perceptual auditory study by listeners and instrumental spectrographic analysis of child speech. Different accuracy and agreement between Russian and Indian experts were shown in recognizing the emotional states of Indian and Russian children by their speech, with more accurate recognition of the emotional state of children in their native language, in acting speech vs spontaneous speech. Both groups of experts recognize the state of anger via acting speech with the high agreement. The difference between the groups of experts was in the definition of joy, sadness, and neutral states depending on the test material with a different agreement. Speech signals with emphasized differences in acoustic patterns were more accurately classified by experts as belonging to emotions of different activation. The data showed that, despite the universality of basic emotions, on the one hand, the cultural environment affects their expression and perception, on the other hand, there are universal non-linguistic acoustic features of the voice that allow us to identify emotions via speech.

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Section: Methodsmentioning

confidence: 99%

The children’s emotional speech recognition by adults: Cross-cultural study on Russian and Tamil language

et al. 2023

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“…In [14], Maximum entropy based automatic prosody labeling framework that exploits both language and speech information was addressed. In [15] , it has been demonstrated a prosodic feature extraction tool to identify regional Tamil dialects by monitoring the parameters duration, F0 and other important values like range, movement and slope. It is important to consider all these methods and parameters while designing prosody model for any language specific speech synthesis system.…”

Section: Other Contributions Of Prosody Modelmentioning

confidence: 99%

Prosody Modeling Techniques for Text-to-Speech Synthesis Systems A Survey

Rajeswari¹,

P²

2012

IJCA

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This paper presents a study on prosody modeling for speech synthesis. Any Text to Speech system comprises of two phases. One is text analysis and second is speech synthesis. The task of text analysis is to find the words and the task of speech synthesis is to generate the speech. To attain this, different models are available such as text as language models, grapheme to phoneme models, full linguistic analysis model and complete prosody generation model. In complete prosody generation model, the quantities like phrasing, stress and the like are determined to generate naturalness bearing synthetic voice. Towards generating such a speech, an explicit prosodic model is required. This makes the speech more understandable. Many researches have been done in this stream, but still better solution is required. In this paper, the strength and weaknesses of different approaches of prosody models are discussed.

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A Class of Neuro-computational Models to Verify Mood Variation in Dialectal Assamese Speech

Agarwalla

Sarma

2014

2014 2nd International Symposium on Computational and Business Intelligence

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Prosodic feature extraction for regional Tamil dialects

Cited by 3 publications

References 3 publications

The children’s emotional speech recognition by adults: Cross-cultural study on Russian and Tamil language

The children’s emotional speech recognition by adults: Cross-cultural study on Russian and Tamil language

Prosody Modeling Techniques for Text-to-Speech Synthesis Systems A Survey

A Class of Neuro-computational Models to Verify Mood Variation in Dialectal Assamese Speech

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