Domain Adversarial for Acoustic Emotion Recognition

DBN-ivector Framework for Acoustic Emotion Recognition

Deep learning and i-vectors have been successfully used in speech and speaker recognition recently. In this work we propose a framework based on deep belief network (DBN) and ivector space modeling for acoustic emotion recognition. We use two types of labels for frame level DBN training. The first one is the vector of posterior probabilities calculated from the GMM universal background model (U...

Adversarial Auto-Encoders for Speech Based Emotion Recognition

Recently, generative adversarial networks and adversarial autoencoders have gained a lot of attention in machine learning community due to their exceptional performance in tasks such as digit classification and face recognition. They map the autoencoder’s bottleneck layer output (termed as code vectors) to different noise Probability Distribution Functions (PDFs), that can be further regularize...

Emotion Recognition using Acoustic and Lexical Features

In this paper we present an innovative approach for utterance-level emotion recognition by fusing acoustic features with lexical features extracted from automatic speech recognition (ASR) output. The acoustic features are generated by combining: (1) a novel set of features that are derived from segmental Mel Frequency Cepstral Coefficients (MFCC) scored against emotion-dependent Gaussian mixtur...

Time domain acoustic parameters for speech recognition

Allophone-based acoustic modeling for Persian phoneme recognition

Phoneme recognition is one of the fundamental phases of automatic speech recognition. Coarticulation which refers to the integration of sounds, is one of the important obstacles in phoneme recognition. In other words, each phone is influenced and changed by the characteristics of its neighbor phones, and coarticulation is responsible for most of these changes. The idea of modeling the effects o...