Epoch is the instant of significant excitation of the vocal-tract system during production of speech. For most voiced speech, the most significant excitation takes place around the instant of glottal closure. Extraction of epochs from speech is a challenging task due to time-varying characteristics of the source and the system. Most epoch extraction methods attempt to remove the characteristics of the vocal-tract system, in order to emphasize the excitation characteristics in the residual. The performance of such methods depends critically on our ability to model the system. In this paper, we propose a method for epoch extraction which does not depend critically on characteristics of the time-varying vocal-tract system. The method exploits the nature of impulse-like excitation. The proposed zero resonance frequency filter output brings out the epoch locations with high accuracy and reliability. The performance of the method is demonstrated using CMU-Arctic database using the epoch information from the electroglottograph as reference. The proposed method performs significantly better than the other methods currently available for epoch extraction. The interesting part of the results is that the epoch extraction by the proposed method seems to be robust against degradations like white noise, babble, high-frequency channel, and vehicle noise.

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Epoch Extraction From Speech Signals

Emotion recognition from speech has emerged as an important research area in the recent past. In this regard, review of existing work on emotional speech processing is useful for carrying out further research. In this paper, the recent literature on speech emotion recognition has been presented considering the issues related to emotional speech corpora, different types of speech features and models used for recognition of emotions from speech. Thirty two representative speech databases are reviewed in this work from point of view of their language, number of speakers, number of emotions, and purpose of collection. The issues related to emotional speech databases used in emotional speech recognition are also briefly discussed. Literature on different features used in the task of emotion recognition from speech is presented. The importance of choosing different classification models has been discussed along with the review. The important issues to be considered for further emotion recognition research in general and in specific to the Indian context have been highlighted where ever necessary.

/pdf/emotion-recognition-from-speech-a-review-2bhwxdu8ml.pdf

Emotion recognition from speech: a review

The pseudo-periodicity of voiced speech can be exploited in several speech processing applications. This requires however that the precise locations of the glottal closure instants (GCIs) are available. The focus of this paper is the evaluation of automatic methods for the detection of GCIs directly from the speech waveform. Five state-of-the-art GCI detection algorithms are compared using six different databases with contemporaneous electroglottographic recordings as ground truth, and containing many hours of speech by multiple speakers. The five techniques compared are the Hilbert Envelope-based detection (HE), the Zero Frequency Resonator-based method (ZFR), the Dynamic Programming Phase Slope Algorithm (DYPSA), the Speech Event Detection using the Residual Excitation And a Mean-based Signal (SEDREAMS) and the Yet Another GCI Algorithm (YAGA). The efficacy of these methods is first evaluated on clean speech, both in terms of reliabililty and accuracy. Their robustness to additive noise and to reverberation is also assessed. A further contribution of the paper is the evaluation of their performance on a concrete application of speech processing: the causal-anticausal decomposition of speech. It is shown that for clean speech, SEDREAMS and YAGA are the best performing techniques, both in terms of identification rate and accuracy. ZFR and SEDREAMS also show a superior robustness to additive noise and reverberation.

Detection of Glottal Closure Instants From Speech Signals: A Quantitative Review

Speech is an effective medium to express emotions and attitude through language. Finding the emotional content from a speech signal and identify the emotions from the speech utterances is an important task for the researchers. Speech emotion recognition has considered as an important research area over the last decade. Many researchers have been attracted due to the automated analysis of human affective behaviour. Therefore a number of systems, algorithms, and classifiers have been developed and outlined for the identification of emotional content of a speech from a person's speech. In this study, available literature on various databases, different features and classifiers have been taken in to consideration for speech emotion recognition from assorted languages.

Databases, features and classifiers for speech emotion recognition: a review

Endowing machines with sensing capabilities similar to those of humans is a prevalent quest in engineering and computer science. In the pursuit of making computers sense their surroundings, a huge effort has been conducted to allow machines and computers to acquire, process, analyze and understand their environment in a human-like way. Focusing on the sense of hearing, the ability of computers to sense their acoustic environment as humans do goes by the name of machine hearing. To achieve this ambitious aim, the representation of the audio signal is of paramount importance. In this paper, we present an up-to-date review of the most relevant audio feature extraction techniques developed to analyze the most usual audio signals: speech, music and environmental sounds. Besides revisiting classic approaches for completeness, we include the latest advances in the field based on new domains of analysis together with novel bio-inspired proposals. These approaches are described following a taxonomy that organizes them according to their physical or perceptual basis, being subsequently divided depending on the domain of computation (time, frequency, wavelet, image-based, cepstral, or other domains). The description of the approaches is accompanied with recent examples of their application to machine hearing related problems.

https://www.mdpi.com/2076-3417/6/5/143/pdf

A Review of Physical and Perceptual Feature Extraction Techniques for Speech, Music and Environmental Sounds

This letter proposes a time-effective method for determining the instants of significant excitation in speech signals. The instants of significant excitation correspond to the instants of glottal closure (epochs) in the case of voiced speech, and to some random excitations like onset of burst in the case of nonvoiced speech. The proposed method consists of two phases: the first phase determines the approximate epoch locations using the Hilbert envelope of the linear prediction residual of the speech signal. The second phase determines the accurate locations of the instants of significant excitation by computing the group delay around the approximate epoch locations derived from the first phase. The accuracy in determining the instants of significant excitation and the time complexity of the proposed method is compared with the group delay based approach.

K.S. Rao

Papers

Determination of Instants of Significant Excitation in Speech Using Hilbert Envelope and Group Delay Function