Wavesurfer - an open source speech tool

By K. Sjölander, J. Beskow. Interspeech, 2000

551 citations recorded by the source corpus.

In the speech technology research community there is an increasing trend to use open source solutions. We present a new tool in that spirit, WaveSurfer, which has been developed at the Centre for Speech Technology at KTH. It has been designed for tasks such as viewing, editing, and labeling of audio data. WaveSurfer is built around a small core to which most functionality is added in the form of plug-ins. The tool has been designed to work on most common platforms and with the aims that it should be easy to configure and extend. WaveSurfer is provided as open source, under the GPL license with the explicit goal that the speech community jointly will improve and expand its scope and capabilities.

Subfields: Artificial Intelligence, Signal Processing

View Paper: https://www.semanticscholar.org/paper/5021746035f84de868b95971e16a7ddbc97b65dd

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

In Signal Processing

Citations in the corpus

Data-driven models for timing feedback responses in a Map Task dialogue system (2014, methodology): “Pitch and intensity (sampled at 10 ms) for each IPU were extracted using ESPS in Wavesurfer/Snack (Sjölander & Beskow, 2000).”
Avian soundscapes and cognitive landscapes: theory, application and ecological perspectives (2011, methodology): “WaveSurfer 1.8.5 (http://sourceforge.net/projects/ wavesurfer) is an open source audio editor for sound visualization and manipulation (Sjolander and Beskow 2000).”
Social signal processing: Survey of an emerging domain (2009, background): “Wavesurfer 1 [177] and Praat 2 [18], implement algorithms extracting the pitch from speech recordings.”
Towards a General, Continuous Model of Turn-taking in Spoken Dialogue using LSTM Recurrent Neural Networks (2017, methodology): “Pitch: The pitch was automatically extracted using the Snack toolkit (Sjölander and Beskow, 2000), transformed into semitones, and then znormalized for the individual speaker.”
A study of gestural feedback expressions (2006, background): “Phonetic correlates for different function of short expressions have been found for a variety of languages: Italian (Cerrato 2002b), Swedish (Cerrato 2002a), English (Hirshberg & Nakatani 1996), Japanese (Ward & Tsukahara 2000), Dutch (Casper 2002).”
Speaking rate and information content in English lingua franca oral presentations (2010, methodology): “Analysis was done using WaveSurfer (Sjölander & Beskow, 2000) to present the speech waveform and enable the measurement of pause length.”
Train&align: A new online tool for automatic phonetic alignment (2012, methodology): “Annotation tools like Praat [1], WaveSurfer [2], etc.”