Semi-automatic phonetic labelling of large corpora

International audienceThe aim of the present paper is to present a methodology to semi-automatically label large corpora. This methodology is based on three main points: using several concurrent automatic stochastic labellers, decomposing the labelling of the whole corpus into an iterative refining process and building a labelling comparison procedure which takes into account phonologic and acoustic-phonetic rules to evaluate the similarity of the various labelling of one sentence. After having detailed these three points, we describe our HMM-based labelling tool and we describe the application of that methodology to the Swiss French POLYPHON database

Two Tools for Semi-automatic Phonetic Labelling of Large Corpora

International audienceThis paper presents two tools allowing a reliable semi-automatic labelling of large corpora : an automatic HMM-based labelling tool and an assessment and decision system to validate the automatically labelled sentences. This decision system uses the results supplied by another automatic labeller and compares their results with a parametrisable comparison process. We also propose an generic methodology to improve the labelling accuracy and to reduce the step of manual verification

VARD2:a tool for dealing with spelling variation in historical corpora

When applying corpus linguistic techniques to historical corpora, the corpus researcher should be cautious about the results obtained. Corpus annotation techniques such as part of speech tagging, trained for modern languages, are particularly vulnerable to inaccuracy due to vocabulary and grammatical shifts in language over time. Basic corpus retrieval techniques such as frequency profiling and concordancing will also be affected, in addition to the more sophisticated techniques such as keywords, n-grams, clusters and lexical bundles which rely on word frequencies for their calculations. In this paper, we highlight these problems with particular focus on Early Modern English corpora. We also present an overview of the VARD tool, our proposed solution to this problem, which facilitates pre-processing of historical corpus data by inserting modern equivalents alongside historical spelling variants. Recent improvements to the VARD tool include the incorporation of techniques used in modern spell checking software

Automatic transcription and phonetic labelling of dyslexic children's reading in Bahasa Melayu

Automatic speech recognition (ASR) is potentially helpful for children who suffer from dyslexia. Highly phonetically similar errors of dyslexic children‟s reading affect the accuracy of ASR. Thus, this study aims to evaluate acceptable accuracy of ASR using automatic transcription and phonetic labelling of dyslexic children‟s reading in BM. For that, three objectives have been set: first to produce manual transcription and phonetic labelling; second to construct automatic transcription and phonetic labelling using forced alignment; and third to compare between accuracy using automatic transcription and phonetic labelling and manual transcription and phonetic labelling. Therefore, to accomplish these goals methods have been used including manual speech labelling and segmentation, forced alignment, Hidden Markov Model (HMM) and Artificial Neural Network (ANN) for training, and for measure accuracy of ASR, Word Error Rate (WER) and False Alarm Rate (FAR) were used. A number of 585 speech files are used for manual transcription, forced alignment and training experiment. The recognition ASR engine using automatic transcription and phonetic labelling obtained optimum results is 76.04% with WER as low as 23.96% and FAR is 17.9%. These results are almost similar with ASR engine using manual transcription namely 76.26%, WER as low as 23.97% and FAR a 17.9%. As conclusion, the accuracy of automatic transcription and phonetic labelling is acceptable to use it for help dyslexic children learning using ASR in Bahasa Melayu (BM

Design and Evaluation of Shared Prosodic Annotation for Spontaneous French Speech: From Expert Knowledge to Non-Expert Annotation

International audienceIn the area of large French speech corpora, there is a demonstrated need for a common prosodic notation system allowing for easy data exchange, comparison, and automatic annotation. The major questions are: (1) how to develop a single simple scheme of prosodic transcription which could form the basis of guidelines for non-expert manual annotation (NEMA), used for linguistic teaching and research; (2) based on this NEMA, how to establish reference prosodic corpora (RPC) for different discourse genres (Cresti and Moneglia, 2005); (3) how to use the RPC to develop corpus-based learning methods for automatic prosodic labelling in spontaneous speech (Buhman et al., 2002; Tamburini and Caini 2005, Avanzi, et al. 2010). This paper presents two pilot experiments conducted with a consortium of 15 French experts in prosody in order to provide a prosodic transcription framework (transcription methodology and transcription reliability measures) and to establish reference prosodic corpora in French

ANALOR. A Tool for Semi-Automatic Annotation of French Prosodic Structure

International audienceIn the area of large speech corpora, there is a definite need for common prosodic notation system based on efficient (semi)- automating tools of prosodic segmentation and labelling. In this context, we present the software program ANALOR, developed in order to process semi-automatically prosodic data. From a text-sound alignment, this computer tool detects major prosodic units, on the basis of global and local melodic variations. That leads to the segmentation of an utterance in prosodic periods. Inside those prosodic periods, prominent syllables are then automatically detected