Wavesurfer - an open source speech tool

Sjölander, Kåre; Beskow, Jonas

doi:10.21437/icslp.2000-849

Cited by 202 publications

(25 citation statements)

References 4 publications

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“…The recorded speech from the overt mode was manually transcribed using the Wavesurfer software package [35] for a separate analysis, but was found useful to provide precise labeling of the speech and non-speech segments for the present study. This was accomplished by shifting a 10 ms non-overlapping frame across the audio recording to identify the onset and offset of the spoken sentence, with the resulting timings from the transcription word boundaries being used as the frame label.…”

Section: Methodsmentioning

confidence: 99%

The Nested Hierarchy of Overt, Mouthed, and Imagined Speech Activity Evident in Intracranial Recordings

Soroush

Riès

Shih³

et al. 2022

Preprint

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Recent studies have demonstrated that it is possible to decode and synthesize various aspects of acoustic speech directly from intracranial measurements of electrophysiological brain activity. In order to continue progressing toward the development of a practical speech neuroprosthesis for the individuals with speech impairments, better understanding and modeling of imagined speech processes are required. The present study uses intracranial brain recordings from participants that performed a speaking task with trials consisting of overt, mouthed, and imagined speech, representing various degrees of decreasing behavioral output. Speech activity detection models are constructed using spatial, spectral, and temporal brain activity features, and the features and model performances are characterized and compared across the three degrees of behavioral output. The results indicate there is a hierarchy in which the relevant channels for the lower behavioral output modes form nested subsets of the relevant channels from the higher behavioral output modes. This provides important insights for the elusive goal of developing more effective imagined speech decoding models with respect to the better-established overt speech decoding counterparts.

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Section: Methodsmentioning

confidence: 99%

The Nested Hierarchy of Overt, Mouthed, and Imagined Speech Activity Evident in Intracranial Recordings

Soroush

Riès

Shih³

et al. 2022

Preprint

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“…Purpose Annotation Tool [69] Annotation TORCS [116] Car simulation OpenCV [123] Image analysis OpenFace [5] Image analysis BioSig -Matlab [117] Biosignal analysis PhysioToolkit [35] Biosignal analysis OpenSmile [30] Speech analysis Praat [9] Speech analysis Snack Sound Toolkit [114] Speech analysis Wavesurfer [115] Speech analysis GPSBabel [36] GPS-to-map conversion Matlab Bayes Net [78] Machine larning Weka [41] Machine learning…”

Section: Toolmentioning

confidence: 99%

“…In terms of signal pre-processing, a wide variety of tools have been used that are usually focused on the analysis of a single signal modality. For instance, BioSig [117] and Physiotoolkit [35] have been used to extract features from biophysiological signals; OpenCV [123] and OpenFace [5] have been used to detect faces and identify regions of interest in face images; OpenSmile [30], Praat [9], Snack [114], and Wavesurfer [115] have been used to perform non-linguistic analysis of speech signals; and GPSBabel [36] has been used to connect GPS signals with other mapping programs.…”

Section: Research Toolsmentioning

confidence: 99%

Driver Emotion Recognition for Intelligent Vehicles

et al. 2020

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Driving can occupy a large portion of daily life and often can elicit negative emotional states like anger or stress, which can significantly impact road safety and long-term human health. In recent decades, the arrival of new tools to help recognize human affect has inspired increasing interest in how to develop emotion-aware systems for cars. To help researchers make needed advances in this area, this article provides a comprehensive literature survey of work addressing the problem of human emotion recognition in an automotive context. We systematically review the literature back to 2002 and identify 63 peer-review published articles on this topic. We overview each study’s methodology to measure and recognize emotions in the context of driving. Across the literature, we find a strong preference toward studying emotional states associated with high arousal and negative valence, monitoring the different states with cardiac, electrodermal activity, and speech signals, and using supervised machine learning to automatically infer the underlying human affective states. This article summarizes the existing work together with publicly available resources (e.g., datasets and tools) to help new researchers get started in this field. We also identify new research opportunities to help advance progress for improving driver emotion recognition.

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“…There are numerous speech technology toolkits, specialising either in speech processing (SFS, [12], Snack, [17]), speech recognition (ISIP, [7], HTK, [19]), dialogue, (CSLU, [15]), annotation (EMU, [2], AGTK, [1]), speech analysis-visualisation (SAPPHIRE, [11], Wavesurfer, [16]). STRAPTk shares several common characteristics with these other toolkits and yet it is different in many aspects because of the specialisation in the domain of speech training.…”

Section: General Speech Application Requirementsmentioning

confidence: 99%

An integrated toolkit deploying speech technology for computer based speech training with application to dysarthric speakers

Hatzis¹,

Green²,

Carmichael³

et al. 2003

8th European Conference on Speech Communication and Technology (Eurospeech 2003)

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Computer based speech training systems aim to provide the client with customised tools for improving articulation based on audio-visual stimuli and feedback. They require the integration of various components of speech technology, such as speech recognition and transcription tools, and a database management system which supports multiple on-the-fly configurations of the speech training application. This paper describes the requirements and d e v e l o p m e n t o f STRAPTk ( www.dcs.shef.ac.uk/spandh/projects/straptk -Speech Training Application Toolkit) from the point of view of developers, clinicians, and clients in the domain of speech training for severely dysarthric speakers. Preliminary results from an extended field trial are presented.

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Wavesurfer - an open source speech tool

Cited by 202 publications

References 4 publications

The Nested Hierarchy of Overt, Mouthed, and Imagined Speech Activity Evident in Intracranial Recordings

The Nested Hierarchy of Overt, Mouthed, and Imagined Speech Activity Evident in Intracranial Recordings

Driver Emotion Recognition for Intelligent Vehicles

An integrated toolkit deploying speech technology for computer based speech training with application to dysarthric speakers

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