FP SIMPLE4ALL deliverable D6.5. Deliverable D6.5. Initial Public Release of Open Source Tools

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1 Deliverable D6.5 Initial Public Release of Open Source Tools The research leading to these results has received funding from the European Community s Seventh Framework Programme (FP7/ ) under grant agreement number Participant no. Participant organisation name Part. short name Country 1 (Coordinator) University of Edinburgh UEDIN UK 2 Aalto University AALTO Finland 3 University of Helsinki UH Finland 4 Universidad Politécnica de Madrid UPM Spain 5 Technical University of Cluj-Napoca UTCN Romania Version 2 (08/11/2013) 1 page 1 of 5

2 Project reference number FP Proposal acronym SIMPLE4ALL Status and Version Complete, proofread, ready for delivery: version 2 Deliverable title Initial Public Release of Open Source Tools Nature of the Deliverable Report (R) Dissemination Level Public (PU) This document is available from WP contributing to the deliverable WP6 WP / Task responsible WP6 / Task T6.3 Editor Rob Clark (UEDIN) Editor address robert@cstr.ed.ac.uk Author(s), in alphabetical order Rob Clark EC Project Officer Pierre Paul Sondag Abstract This report documents the initial release of Open source tools resulting from SIMPLE4ALL Version 2 (08/11/2013) 2 page 2 of 5

3 Contents 1 Introduction 4 2 Tools ALISA Speech Alignment tool Ossian New language front end building tool Norma A Text normalisation tool Dexter A (Style) diarisation tool The Tundra corpus 5 4 Release of the tools 5 Version 2 (08/11/2013) 3 page 3 of 5

4 1 Introduction The software that has been developed by SIMPLE4ALL is sufficiently complex in nature that we have decided to release it as a number of different packages, each with a specific task in mind. This allows users that are only interested in parts of the system to download just the tools that are of interest to them. Each tool contains a small example set of data and a worked example of how to use the tool on that data. 2 Tools 2.1 ALISA Speech Alignment tool ALISA is a lightly-supervised speech segmentation and alignment tool which uses a two step approach for the task of aligning speech with imperfect transcripts: 1. sentence-level speech segmentation 2. sentence-level speech and text alignment Both processes are fully automated and require as little as 10 minutes of manually labelled speech to initialise the models: labels of inter-sentence silence segments for the segmentation step, and orthographic transcripts of these sentences for the acoustic models used by the aligner. The tool can be applied to any language with an alphabetic writing system and can align up to 75% of the original data with a sentence error rate of less then 8% and a word error rate of less than 1%. The segmentation step uses two Gaussian Mixture Models (GMM) trained on speech and silence segments, respectively, and determines a threshold for the short pause vs. longer silence discrimination. For the alignment, we use iteratively self-trained acoustic models and highly-restricted word networks built from the available text resource in order to drive a grapheme-level Viterbi decoder over the speech data. For further technical detail of this method, see Section 2 of D1.4 and Section 5 of D Ossian New language front end building tool Ossian is a python-based tool for automatically building speech synthesis front ends.the framework consists of a sequence of utterance processors, each of which accepts and enriches an XML representation of an utterance. The framework is designed to be flexible so that the nature and number of processors and their roles can be configured by a user. A range of processors are supplied by the system, but it is also relatively easy to code new ones. For further technical details about this system see D2.2. Ossian can easily be used in conjunction with ALISA to prepare the necessary dataset for building a complete TTS system. 2.3 Norma A Text normalisation tool Norma is a translation-based [text] normaliser (TBN). This is a toolkit for building text normalisation models based on open-source tools, is able to learn how to normalise Non-Standard Words (NSW) for a Text-To-Speech system (TTS) in any language. It is a data-driven toolkit, where the developer provides the system with a set of example NSWs and how they normalise (i.e., are spelled-out as a sequence of standard words). These examples are then used to train an automatic translation system which can then be integrated into the front-end of the TTS system in order to process not only the provided examples, but also other cases through the generalisation capabilities of the translation-based normaliser. Version 2 (08/11/2013) 4 page 4 of 5

5 2.4 Dexter A (Style) diarisation tool Dexter is a complete diarisation system based on existing open source toolkits. The system extracts the most relevant acoustic features from an untranscribed speech file and then diarises it, by detecting who is speaking when. All processing is based on unsupervised techniques, not needing any labels for training or fine tuning. Dexter also includes a style diariser which is a subset of the tools specialised in detecting narrator and non-narrator styles in a mono-speaker audiobook. If the book has been transcribed and aligned in a sentence-by-sentence basis, the style diarisation process can utilise this information and give better performance, although this is not mandatory. For further technical detail of this system and the methods it is based on, see Section 2 of D The Tundra corpus In addition to the above tools, we have made our first public release of data. The Tundra corpus is a collection found data consisting of 14 audiobooks in 14 languages: Bulgarian, Danish, Dutch, English, Finnish, French, German, Hungarian, Italian, Polish, Portuguese, Romanian, Russian and Spanish. All have been segmented and aligned for speech synthesis use. For further information about how this corpus was prepared, please see Section 6 of D Release of the tools The tools will be downloadable from the outputs 1 section of the SIMPLE4ALL public website by the end of November Version 2 (08/11/2013) 5 page 5 of 5

ECHA -term User Guide

ECHA -term User Guide 1 / 27 Table of contents Introduction... 3 Content... 3 Reliability of data... 4 Languages... 5 Target users... 5 Terminology principles... 5 Domain classification... 6 Localised