Emotion Recognition using Speech Features by K. Sreenivasa Rao

By K. Sreenivasa Rao

“Emotion acceptance utilizing Speech positive factors” offers insurance of emotion-specific positive factors found in speech. the writer additionally discusses appropriate versions for taking pictures emotion-specific details for distinguishing varied feelings. The content material of this publication is critical for designing and constructing traditional and complex speech structures. during this short, Drs. Rao and Koolagudi lead a dialogue of ways emotion-specific info is embedded in speech and the way to obtain emotion-specific wisdom utilizing acceptable statistical types. also, the authors offer information regarding exploiting a number of evidences derived from a number of positive factors and types. The received emotion-specific wisdom turns out to be useful for synthesizing feelings. positive factors comprises dialogue of: • worldwide and native prosodic beneficial properties at syllable, observe and word degrees, precious for taking pictures emotion-discriminative info; • Exploiting complementary evidences got from excitation assets, vocal tract platforms and prosodic positive factors so as to increase the emotion reputation functionality; • Proposed multi-stage and hybrid versions for bettering the emotion popularity functionality. This short is for researchers operating in components relating to speech-based items similar to cell phone production businesses, car businesses, and leisure items in addition to researchers focused on uncomplicated and utilized speech processing research.

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Additional info for Emotion Recognition using Speech Features

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F0 information is analyzed for emotion classification and it is reported that minimum, maximum and median values of F0 and slopes of F0 contours are emotion salient features. Around 80% of emotion recognition accuracy is achieved, using proposed F0 features with a K-nearest neighbor classifier [27]. Short time supra-segmental features such as pitch, energy, formant locations and their bandwidths, dynamics of pitch, energy and formant contours, speaking rate are used for analyzing the emotions [1].

No. Features Purpose and approach References 01 [105] 02 Initially 86 prosodic features are used, later best 6 features are chosen from the list 35 dimensional prosodic feature vectors including pitch, energy, and duration are used 03 Pitch and power based features are extracted from frame, syllable, and word levels 04 Duration, energy, and pitch based features 05 Eight static prosodic features and voice quality features 06 Energy, pitch and duration based features 07 Prosody and voice quality based features Identification of emotions in the Basque language.

Pitch, tempo, and stress are used for emotion synthesis Recognition. Phonation, articulation and prosody are used to classify four emotions Recognition Recognition. 2 Emotional Speech Corpora: A Review 21 Anger, joy, and sadness (03) 29 Italian emotional speech corpus 32 Anger, disgust, fear, joy, sadness, and surprise (06) Russian emotional speech corpus 30 Anger, fear, happiness, neutral, sadness, and surprise (06), ten sentences are recorded per emotion in different sessions Swedish emotional speech corpus 31 Happiness and neutral (02) Emotions Sl.

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