Emotion Robust Speech Recognition using Speech Transformation

Speech Emotion Recognition Using Scalogram Based Deep Structure

Speech Emotion Recognition (SER) is an important part of speech-based Human-Computer Interface (HCI) applications. Previous SER methods rely on the extraction of features and training an appropriate classifier. However, most of those features can be affected by emotionally irrelevant factors such as gender, speaking styles and environment. Here, an SER method has been proposed based on a concat...

Spectro-temporal Modulations for Robust Speech Emotion Recognition Spectro-temporal Modulations for Robust Speech Emotion Recognition

Robust Recognition of Emotion from Speech

This paper presents robust recognition of selected emotions from salient spoken words. The prosodic and acoustic features were used to extract the intonation patterns and correlates of emotion from speech samples in order to develop and evaluate models of emotion. The computed features are projected using a combination of linear projection techniques for compact and clustered representation of ...

Improving the performance of MFCC for Persian robust speech recognition

The Mel Frequency cepstral coefficients are the most widely used feature in speech recognition but they are very sensitive to noise. In this paper to achieve a satisfactorily performance in Automatic Speech Recognition (ASR) applications we introduce a noise robust new set of MFCC vector estimated through following steps. First, spectral mean normalization is a pre-processing which applies to t...

Robust Speech Recognition Using Speech Enhancement

Automatic Speech Recognition (ASR) has matured into a technology which is becoming more common in our everyday lives, and is emerging as a necessity to minimise driver distraction when operating in-car systems such as navigation and infotainment. In “noise-free” environments, word recognition performance of these systems has been shown to approach 100%, however this performance degrades rapidly...