Evaluation of influence of spectral and prosodic features on GMM classification of Czech and Slovak emotional speech

Abstract

This article analyzes and compares influence of different types of spectral and prosodic features for Czech and Slovak emotional speech classification based on Gaussian mixture models (GMM). Influence of initial setting of parameters (number of mixture components and used number of iterations) for GMM training process was analyzed, too. Subsequently, analysis was performed to find how correctness of emotion classification depends on the number and the order of the parameters in the input feature vector and on the computation complexity. Another test was carried out to verify the functionality of the proposed two-level architecture comprising the gender recognizer and of the emotional speech classifier. Next tests were realized to find dependence of some negative aspect (processing of the input speech signal with too short time duration, the gender of a speaker incorrectly determined, etc.) on the stability of the results generated during the GMM classification process. Evaluations and tests were realized with the speech material in the form of sentences of male and female speakers expressing four emotional states (joy, sadness, anger, and a neutral state) in Czech and Slovak languages. In addition, a comparative experiment using the speech data corpus in other language (German) was performed. The mean classification error rate of the whole classifier structure achieves about 21% for all four emotions and both genders, and the best obtained error rate was 3.5% for the sadness style of the female gender. These values are acceptable in this first stage of development of the GMM classifier. On the other hand, the test showed the principal importance of correct classification of the speaker gender in the first level, which has heavy influence on the resulting recognition score of the emotion classification. This GMM classifier should be used for evaluation of the synthetic speech quality after applied voice conversion and emotional speech style transformation.

Within and cross-corpus speech emotion recognition using latent topic model-based features

“…Unlike LDA, each word in an RSM is generated by multiple topics, leading to distributed and richer representations. Furthermore, the topics are inferred in a single pass via Equation 6, offering a significant reduction in computational complexity.…”

“…Segmental approaches operate directly on these frames using static Gaussian mixture models (GMM) [6], dynamic hidden Markov models (HMM) [7,8], or their variants [9]. Based on studies indicating the suprasegmental behavior of emotions [2,10], turn-level features have shown to significantly outperform HMM-based approaches.…”

GMM Classification of Text-to-Speech Synthesis: Identification of Original Speaker’s Voice

“…For implementation of the supra-segmental parameters of speech, the statistical types-median values, range of values, std, and/or relative maximum and minimum were used in the feature vectors. The length of the input feature vector N f eat = 16 was experimentally chosen in correspondence with the obtained results of our previous research [16]. The three tested feature sets were: P1 consisting of the basic spectral features together with the supra-segmental parameters, P2 consisting of the supplementary spectral features and the supra-segmental parameters, and P3 being a mix of the basic and the supplementary spectral features with the prosodic parameters.…”

Automatic Text-Independent Artifact Detection, Localization, and Classification in the Synthetic Speech

“…The extended vector PN15 consists of the features with MUTI SF < N SFC and h = 1: En c0 , En r0 , HNR, S centr , S spread , S skew , S tilt, SFM, SHE, F0 DIFF , L ZCR , F ZCR , F 1 /F 2 , J abs , AP rel . According to the results published in [10], [20], the length of the input data vector for GMM training and testing was set to 16.…”