COVAREP — A collaborative voice analysis repository for speech technologies

By G. Degottex, John Kane, Thomas Drugman, Tuomo Raitio, Stefan Scherer. 2014

834 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Speech & Audio, Signal Processing

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Citations in the corpus

Multi-attention Recurrent Network for Human Communication Comprehension (2018, methodology): “Acoustic We use COVAREP (Degottex et al. 2014) to extract low level acoustic features including 12 Mel-frequency cepstral coefﬁcients (MFCCs), pitch tracking and voiced/unvoiced segmenting features, glottal source parameters, peak slope parameters and maxima dispersion quotients.”
Memory Fusion Network for Multi-view Sequential Learning (2018, methodology): “Acoustic View For the audio view, the software CO-VAREP (Degottex et al. 2014) is used to extract acoustic features including 12 Mel-frequency cepstral coefﬁ-cients, pitch tracking and voiced/unvoiced segmenting features (Drugman and Alwan 2011), glottal source parameters (Childers and Lee 1991; Dru”
Words Can Shift: Dynamically Adjusting Word Representations Using Nonverbal Behaviors (2018, methodology): “Acoustic Features : we use the COVAREP (Degottex et al. 2014) acoustic analysis framework for feature extraction.”
Multimodal Language Analysis in the Wild: CMU-MOSEI Dataset and Interpretable Dynamic Fusion Graph (2018, methodology): “Acoustic: We use the COVAREP software (Degottex et al., 2014) to extract acoustic features including 12 Mel-frequency cepstral coefficients, pitch, voiced/unvoiced segmenting features (Drugman and Alwan, 2011), glottal source parameters (Drugman et al.”
Multimodal sentiment analysis with word-level fusion and reinforcement learning (2017, methodology): “For audio inputs, we use COVAREP [7] to extract acoustic features including 12 Mel-frequency cepstral coefficients (MFCCs), pitch tracking and voiced/unvoiced segmenting features, glottal source parameters, peak slope parameters and maxima dispersion quotients.”
AVEC 2016: Depression, Mood, and Emotion Recognition Workshop and Challenge (2016, methodology): “2), a freely available open source Matlab and Octave toolbox for speech analyses [8](2).”
Factorized Multimodal Transformer for Multimodal Sequential Learning (2019, methodology): “Acoustic: COVAREP (Degottex et al., 2014) is used to extract the following features: fundamental frequency, quasi open quotient (Kane & Gobl, 2013), normalized amplitude quotient, glottal source parameters (H1H2, Rd, Rd conf) (Drugman et al., 2012), Voiced/Unvoiced segmenting features (VUV) (Drugman”
Found in Translation: Learning Robust Joint Representations by Cyclic Translations Between Modalities (2018, positive): “Following previous work (Liang et al. 2018), GloVe word embeddings (Pennington, Socher, and Manning 2014), Facet (iMotions 2017), and COVAREP (Degottex et al. 2014) features are extracted for the language, visual and acoustic modalities respectively 2 .”
Multimodal Affective Analysis Using Hierarchical Attention Strategy with Word-Level Alignment (2018, methodology): “The key differences between this work and the proposed architecture are: (i) we design a fine-tunable hierarchical attention structure to extract word-level features for each individual modality, rather than simply using the initialized textual embedding and extracted LLDs from COVAREP (Degottex et ”
Efficient Low-rank Multimodal Fusion With Modality-Specific Factors (2018, methodology): “Acoustic We use COVAREP acoustic analysis framework (Degottex et al., 2014) to extract a set of low-level acoustic features, including 12 Mel frequency cepstral coefficients (MFCCs), pitch, voiced/unvoiced segmentation, glottal source, peak slope, and maxima dispersion quotient features.”
Tensor Fusion Network for Multimodal Sentiment Analysis (2017, methodology): “…IAIF (Drug-man et al., 2012; Alku, 1992; Alku et al., 2002, 1997; Titze and Sundberg, 1992; Childers and Lee, 1991)), peak slope parameters (Degottex et al., 2014), maxima dispersion quotients (MDQ) (Kane and Gobl, 2013), and estimations of the R d shape parameter of the Liljencrants-Fant…”
AVEC 2017: Real-life Depression, and Affect Recognition Workshop and Challenge (2017, positive): “2), a freely available open source Matlab and Octave toolbox for speech analyses [5].”
Computational Analysis of Persuasiveness in Social Multimedia: A Novel Dataset and Multimodal Prediction Approach (2014, methodology): “Following common approaches for conducting automatic speech analysis [39], we extracted various speech features related to pitch, formants, voice qualities and mel-frequency cepstral coefficients (MFCCs) using a publicly available software called Covarep [10].”
Integrating Multimodal Information in Large Pretrained Transformers (2020, methodology): “Acoustic: COVAREP (Degottex et al., 2014) is used to extract the following relevant features: fundamental frequency, quasi open quotient, normalized amplitude quotient, glottal source parameters (H1H2, Rd, Rd conf), VUV, MDQ, the ﬁrst 3 formants, PSP, HMPDM 0-24 and HM-PDD 0-12, spectral tilt/slope…”
MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis (2020, methodology): “The acoustic features contain various low-level statistical audio functions extracted from COVAREP [10] – an acoustic analysis framework.”