Graph Learning Based Speaker Independent Speech Emotion Recognition

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Speaker Independent Speech Recognition Using Hidden Markov Models for Persian Isolated Words

Speaker-independent emotion recognition based on feature vector classification

This paper proposes a new feature vector classification for speech emotion recognition. The conventional feature vector classification applied to speaker identification categorized feature vectors as overlapped and non-overlapped. This method discards all of the overlapped vectors in model training, while non-overlapped vectors are used to reconstruct corresponding speaker models. Although the ...

Speaker Emotion Recognition Based on Speech Features and Classification Techniques

Speech Processing has been developed as one of the vital provision region of Digital Signal Processing. Speaker recognition is the methodology of immediately distinguishing who is talking dependent upon special aspects held in discourse waves. This strategy makes it conceivable to utilize the speaker's voice to check their character and control access to administrations, for example voice diali...

Speaker dependent emotion recognition using speech signals

This paper examines three algorithms to recognize speaker’s emotion using the speech signals. Target emotions are happiness, sadness, anger, fear, boredom and neutral state. MLB(Maximum-Likelihood Bayes), NN(Nearest Neighbor) and HMM(Hidden Markov Model) algorithms are used as the pattern matching techniques. In all cases, pitch and energy are used as the features. The feature vectors for MLB a...