Rashmirekha Ram

Design of FIS-Based Model for Emotional Speech Recognition

Human beings have emotions associated with their acts and speeches. The emotional expressions vary with moods and situations. Speech is an important medium through which people express their feelings. Prosodic, spectral, and other parameters of speech vary with the emotions. The ability to represent the emotional speech varies with the type of features chosen. In an attempt to recognize such an emotional content of speech, one of the spectral features (linear prediction coefficients), have been first tested by the fuzzy interference system. Next to it hybridization of LPC features with different prosodic features were compared with LPC features for recognition accuracy. Results show that the hybridization of features can classify emotions better with the FIS system.

Comparative Analysis of EMD and VMD Algorithm in Speech Enhancement

Signal enhancement is useful in many areas like social, medicine and engineering. It can be utilized in data mining approach for social and security aspects. Signal decomposition method is an alternative choice due to the elimination of noise and signal enhancement. In this paper, two different algorithms such as Empirical Mode Decomposition (EMD) and Variational Mode Decomposition (VMD) are used. The bands are updated concurrently and adaptively in each mode. That performs better than the traditional methods for non-recursive signals. Further it has been investigated that VMD outperforms EMD due to its self-optimization methods as well as adaptively using Wiener filter. It is shown in the result section. Different noise levels as 0dB, 5dB, 10dB and 15dB are considered for input signal.

Application of Variational Mode Decomposition on Speech Enhancement

Enhancement of speech signal and reduction of noise from speech is still a challenging task for researchers. Out of many methods signal decomposition method attracts a lot in recent years. Empirical Mode Decomposition (EMD) has been applied in many problems of decomposition. Recently Variational Mode Decomposition (VMD) is introduced as an alternative to it that can easily separate the signals of similar frequencies. This paper proposes the signal decomposition algorithm as VMD for denoising and enhancement of speech signal. VMD decomposes the recorded speech signal into several modes. Speech contaminated with different types of noise is adaptively decomposed into various components is said to be Intrinsic Mode Functions (IMFs) by sifting process as in Empirical Mode decomposition (EMD) method. Next to it the denoising technique is applied using VMD. Each of the decomposed modes is compact. The simulation result shows that the proposed method is well suited for the speech enhancement and removal of noise by restoring the original

Impulsive noise cancellation for speech enhancement using state space adaptive algorithm

The occurrence of noise in almost all types of signals is natural. Though the noise variants are many, the impulsive noise in signal highly affects its quality. In this piece of work, speech signal is considered for enhancement that is contaminated with impulsive noise. Generally, hiccups create such type of noise due to tiredness or myoclonic problem of human subjects. Removal of this type of impulsive noise can enhance the speech signal and can be used in case of recognition, security and in the field of medicine. The popular recursive least mean square (RLS) algorithm has been used for this purpose. Also the state space variant of RLS (SSRLS) application enhances the result and can be used for real time applications. The result shows its performance in terms of signal to noise ratio (SNR) and the visualization of the speech signal.

Recognition of fear from speech using adaptive algorithm with MLP classifier

Clarity and intelligibility in speech signal demands removal of noise and interference associated with the signal at the source. This poses further challenge when the speech signal is colored with human emotions. In this work, the authors have taken a novel step to enhance the emotional speech signal adaptively before classification. Most popular adaptive algorithm such as Least mean square (LMS), Normalized least mean squares (NLMS) and Recursive least square (RLS) has been put to test to obtain enhanced speech emotions. Neural network based Multilayer perceptron (MLP) classifier is used to recognize fear speech emotion as against neutral voices using effective Linear Prediction coefficients (LPCs). The accuracy has improved to approximately 77% with enhanced signal. The increased accuracy of this signal has been witnessed with RLS algorithm as against the noisy signal with corresponding algorithm.

Fractional Segmental Transform for Speech Enhancement

The Fractional Fourier Transform (FrFT) can be interpreted as a rotation in the time-frequency plane with an angle α. It describes the speech signal characteristics as the signal changes from time to frequency domain. However, to locate the fractional Fourier domain frequency contents and multicomponent analysis of nonlinear chirp like signals such as speech the Short-Time FrFT (SFrFT) can provide an improved time-frequency resolution. By representing the time and fractional frequency domain information simultaneously, the SFrFT can filter out cross terms and distortion in a signal adequately for better signal enhancement. The method has experienced with better Signal to Noise Ratio (SNR) and Perceptual Evaluation of Speech Quality (PESQ) under different noisy conditions as compared to the conventional FrFT in our results.

Deep Neural Network Based Speech Enhancement

Enhancement of the speech signal is an essential task in the adverse environment. Several algorithms have been designed from several years to improve the quality. Mostly Neural Network and its variants are utilized for classification purpose. This paper exhibits the speech enhancement method based on the Deep Neural Network (DNN) to improve the quality and to increase the Signal-to-Noise Ratio of the speech signal. Different hidden layers are set to test the results. The audio features are extracted by using the short time Fourier transforms. The use of audio features improves the speech enhancement performance of DNN. Segmental Signal-to-Noise Ratio (SegSNR) and Perceptual Evaluation of Speech Quality (PESQ) are measured to test the results.