• Shubham Venkatesh Sarwade
• V V Yerigeri

Automatic speech emotion recognition can fail to a certain extent when confronted with emotionally distorted speech. Great efforts have been spent so far to cope with noise conditions or speaker’s characteristics. Yet, adaptation to the emotional condition of the speaker could help to further improve the overall performance. In this paper we reviewed different classifiers for speech emotion recognition, with performance parameters and a robust and reliable recognition of the speaker’s emotional state by acoustic features only prior to speech recognition itself. Thereby we can load according emotional speech models. In this work we introduce an optimal feature set for this task selected by Sequential Floating Search Methods. The set comprises high-level prosodic features extraction and resembling utterance-wise statistic analysis of low-level contours as pitch, higher order formants, energy, and spectral development.

Emotion Speech Recognition, Features Extraction, Classifiers

"Classifiers for speech emotion recognition: A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.478-480, June-2019, Available :http://www.jetir.org/papers/JETIR1906215.pdf

"Classifiers for speech emotion recognition: A Review", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp478-480, June-2019, Available at : http://www.jetir.org/papers/JETIR1906215.pdf