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.

https://www.speech.kth.se/wavesurfer/wsurf_icslp00.pdf

Wavesurfer - an open source speech tool.

`Linguistic annotation' covers any descriptive or analytic notations applied to raw language data. The basic data may be in the form of time functions -- audio, video and/or physiological recordings -- or it may be textual. The added notations may include transcriptions of all sorts (from phonetic features to discourse structures), part-of-speech and sense tagging, syntactic analysis, `named entity' identification, co-reference annotation, and so on. While there are several ongoing efforts to provide formats and tools for such annotations and to publish annotated linguistic databases, the lack of widely accepted standards is becoming a critical problem. Proposed standards, to the extent they exist, have focussed on file formats. This paper focuses instead on the logical structure of linguistic annotations. We survey a wide variety of existing annotation formats and demonstrate a common conceptual core, the annotation graph. This provides a formal framework for constructing, maintaining and searching linguistic annotations, while remaining consistent with many alternative data structures and file formats.

A Formal Framework for Linguistic Annotation

https://repository.upenn.edu/cgi/viewcontent.cgi?article=1112&context=cis_reports

A formal framework for linguistic annotation

http://languagelog.ldc.upenn.edu/myl/Barras2001.pdf

Transcriber: Development and use of a tool for assisting speech corpora production

2005 Special Issue: Challenges in real-life emotion annotation and machine learning based detection

This paper describes the first version of “Transcriber”, a tool for segmenting, labeling and transcribing speech. It is developed under Unix in the Tcl/Tk script language with extensions in C, and is available as free software. The environment offers the basic functions necessary for segmenting, labeling and transcribing long duration signals. The signal editor and the text editor are integrated and synchronized in order to display and play the current segment. The output is in a standard SGML format. Multiple languages are supported. The tool can be ported to various platforms and is very flexible so that new functions can be easily added. We hope that such a portable, widely available and flexible tool will benefit the whole community and make it easier to develop and share corpora.

/pdf/transcriber-a-free-tool-for-segmenting-labeling-and-tvho6n51f3.pdf

Transcriber: a free tool for segmenting, labeling and transcribing speech

Transcriber is a tool for manual annotation of large speech files. It was originally designed for the broadcast news transcription task. The annotation file format was derived from previous formats used for this task, and many related features were hard-coded. In this paper we present a generalization of the tool based on the annotation graph formalism, and on a more modular design. This will allow us to address new tasks, while retaining Transcriber’s simple, crisp user-interface which is critical for user acceptance.

/pdf/transcribing-with-annotation-graphs-53aanogjik.pdf

Transcribing with Annotation Graphs.

The Linguistic Data Consortium (LDC), an open consortium of universities, companies and government research laboratories, creates, collects and distributes speech and text databases, lexicons, and other resources for research and development purposes. The LDC has published more than 200 CD-ROMs for use by speech recognition engineers, natural language processing researchers, and linguists, at more than 130 member organizations and 390 non-member sites. The volume of LDC data roughly doubles every year. Few organizations have been able to afford to store and index all LDC data, or to develop the software needed for efficient search and retrieval. Therefore, we have established a webaccessible digital library (accessible via http://www.ldc.upenn.edu), containing all LDC data, including data not yet published on CD-ROM, accessible through a variety of sophisticated and efficient search and display algorithms.

Zhibiao Wu

Papers

Transcriber: Development and use of a tool for assisting speech corpora production

Transcriber: a free tool for segmenting, labeling and transcribing speech

Transcribing with Annotation Graphs.

LDC online: a digital library for linguistic research and development