COVAREP &amp;#x2014; A collaborative voice analysis repository for speech technologies

Degottex, Gilles; Kane, John; Drugman, Thomas; Raitio, Tuomo; Scherer, Stefan

doi:10.1109/icassp.2014.6853739

Cited by 436 publications

(243 citation statements)

References 40 publications

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“…Lastly, the interview ended with a 'cool-down' phase, which ensured that participants would not leave the interview in a distressed state. The depression label provided includes a PHQ-8 1 score (scale from 0 to 24) as well as a binary depression class label, i.e., score >= 10. which uses Covarep (Degottex et al, 2014) and Parsey McParseface (Andor et al, 2016) to extract voice and syntax features.…”

Section: Datasetmentioning

confidence: 99%

See 1 more Smart Citation

A Linguistically-Informed Fusion Approach for Multimodal Depression Detection

Morales¹,

Scherer²,

Levitan³

2018

Proceedings of the Fifth Workshop on Computational Linguistics And Clinical Psychology: From Keyboard to Clinic

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Section: Datasetmentioning

confidence: 99%

“…In order to extract features from the voice, OpenMM employs Covarep (Degottex et al, 2014). The audio features extracted include prosodic, voice quality, and spectral features.…”

Section: Voicementioning

confidence: 99%

A Linguistically-Informed Fusion Approach for Multimodal Depression Detection

Morales¹,

Scherer²,

Levitan³

2018

Proceedings of the Fifth Workshop on Computational Linguistics And Clinical Psychology: From Keyboard to Clinic

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“…Code and data repositories such as Codalab [8] provide an environment for reproducible research, while repositories such as Covarep [9] strive to make sharing of implementations easier. In the speech community, WebASR [10] and CloudCAST [11] aim to provide a shared infrastructure, and some access to components for researchers, rather than treating ASR as a black box.…”

Section: Related Workmentioning

confidence: 99%

Virtual Machines and Containers as a Platform for Experimentation

et al. 2016

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Research on computational speech processing has traditionally relied on the availability of a relatively large and complex infrastructure, which encompasses data (text and audio), tools (feature extraction, model training, scoring, possibly on-line and off-line, etc.), glue code, and computing. Traditionally, it has been very hard to move experiments from one site to another, and to replicate experiments. With the increasing availability of shared platforms such as commercial cloud computing platforms or publicly funded super-computing centers, there is a need and an opportunity to abstract the experimental environment from the hardware, and distribute complete setups as a virtual machine, a container, or some other shareable resource, that can be deployed and worked with anywhere.In this paper, we discuss our experience with this concept and present some tools that the community might find useful. We outline, as a case study, how such tools can be applied to a naturalistic language acquisition audio corpus.

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“…However, the difficulty in correctly extracting those glottal and source features has resulted in limited improvements of performance when combined when cepstral-like features. Fortunately, a recent software repository known as COVAREP (Degottex, Kane, Drugman, Raitio, & Scherer, 2014) provides state-ofthe-art open-source glottal and voice source extraction tools which will surely help to improve their contribution to global performance.…”

Section: Multi-level Extraction Of the Individualizing Informationmentioning

confidence: 99%

Evaluating Automatic Speaker Recognition systems: An overview of the NIST Speaker Recognition Evaluations (1996-2014)

González-Rodríguez¹

2014

loquens

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Automatic Speaker Recognition systems show interesting properties, such as speed of processing or repeatability of results, in contrast to speaker recognition by humans. But they will be usable just if they are reliable. Testability, or the ability to extensively evaluate the goodness of the speaker detector decisions, becomes then critical. In the last 20 years, the US National Institute of Standards and Technology (NIST) has organized, providing the proper speech data and evaluation protocols, a series of text-independent Speaker Recognition Evaluations (SRE). Those evaluations have become not just a periodical benchmark test, but also a meeting point of a collaborative community of scientists that have been deeply involved in the cycle of evaluations, allowing tremendous progress in a specially complex task where the speaker information is spread across different information levels (acoustic, prosodic, linguistic…) and is strongly affected by speaker intrinsic and extrinsic variability factors. In this paper, we outline how the evaluations progressively challenged the technology including new speaking conditions and sources of variability, and how the scientific community gave answers to those demands. Finally, NIST SREs will be shown to be not free of inconveniences, and future challenges to speaker recognition assessment will also be discussed. NIST de reconocimiento de locutor (1996NIST de reconocimiento de locutor ( -2014.-Los sistemas automáticos de reconocimiento de locutor son críticos para la organización, etiquetado, gestión y toma de decisiones sobre grandes bases de datos de voces de diferentes locutores. Con el fin de procesar eficientemente tales cantidades de información de voz, necesitamos sistemas muy rápidos y, al no estar libre de errores, lo suficientemente fiables. Los sistemas actuales son órdenes de magnitud más rápidos que tiempo real, permitiendo tomar decisiones automáticas instantáneas sobre enormes cantidades de conversaciones. Pero tal vez la característica más interesante de un sistema automático es la posibilidad de ser analizado en detalle, ya que su rendimiento y fiabilidad puede ser evaluada de manera ciega sobre cantidades enormes de datos en una gran diversidad de condiciones. En los últimos 20 años, el Instituto Nacional de Estándares y Tecnología (NIST) de EE. UU. ha organizado, proporcionando los datos de voz y protocolos de evaluación adecuada, una serie de evaluaciones de reconocimiento de locutor independiente del texto. Esas evaluaciones se han convertido no sólo en una prueba comparativa periódica, sino también en punto de encuentro de una comunidad colaborativa de científicos que han estado profundamente involucrados en el ciclo de evaluaciones, lo que ha permitido un enorme progreso en una tarea especialmente compleja en la que la información individualizadora del locutor se encuentra dispersa en diferentes niveles de información (acústica, prosódica, lingüística...) y está fuertemente afectada por factores de variabilidad intrínsecos y extrínsecos al ...

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COVAREP — A collaborative voice analysis repository for speech technologies

Cited by 436 publications

References 40 publications

A Linguistically-Informed Fusion Approach for Multimodal Depression Detection

A Linguistically-Informed Fusion Approach for Multimodal Depression Detection

Virtual Machines and Containers as a Platform for Experimentation

Evaluating Automatic Speaker Recognition systems: An overview of the NIST Speaker Recognition Evaluations (1996-2014)

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