pwc-archive/papers-with-abstracts huggingface dataset:
Phonetic Inventory for an Arabic Speech Corpus
Authors: Nawar Halabi, Mike Wald
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Speech Synthesis

Corpus design for speech synthesis is a well-researched topic in languages such as English compared to Modern Standard Arabic, and there is a tendency to focus on methods to automatically generate the orthographic transcript to be recorded (usually greedy methods). In this work, a study of Modern Standard Arabic (MSA) phonetics and phonology is conducted in order to create criteria for a greedy method to create a speech corpus transcript for recording. The size of the dataset is reduced a number of times using these optimisation methods with different parameters to yield a much smaller dataset with identical phonetic coverage than before the reduction, and this output transcript is chosen for recording. This is part of a larger work to create a completely annotated and segmented speech corpus for MSA.
AIMU: Actionable Items for Meeting Understanding
Authors: Yun-Nung Chen, Dilek Hakkani-T{\"u}r
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

With emerging conversational data, automated content analysis is needed for better data interpretation, so that it is accurately understood and can be effectively integrated and utilized in various applications. ICSI meeting corpus is a publicly released data set of multi-party meetings in an organization that has been released over a decade ago, and has been fostering meeting understanding research since then. The original data collection includes transcription of participant turns as well as meta-data annotations, such as disfluencies and dialog act tags. This paper presents an extended set of annotations for the ICSI meeting corpus with a goal of deeply understanding meeting conversations, where participant turns are annotated by actionable items that could be performed by an automated meeting assistant. In addition to the user utterances that contain an actionable item, annotations also include the arguments associated with the actionable item. The set of actionable items are determined by aligning human-human interactions to human-machine interactions, where a data annotation schema designed for a virtual personal assistant (human-machine genre) is adapted to the meetings domain (human-human genre). The data set is formed by annotating participants{'} utterances in meetings with potential intents/actions considering their contexts. The set of actions target what could be accomplished by an automated meeting assistant, such as taking a note of action items that a participant commits to, or finding emails or topic related documents that were mentioned during the meeting. A total of 10 defined intents/actions are considered as actionable items in meetings. Turns that include actionable intents were annotated for 22 public ICSI meetings, that include a total of 21K utterances, segmented by speaker turns. Participants{'} spoken turns, possible actions along with associated arguments and their vector representations as computed by convolutional deep structured semantic models are included in the data set for future research. We present a detailed statistical analysis of the data set and analyze the performance of applying convolutional deep structured semantic models for an actionable item detection task. The data is available at http://research.microsoft.com/ projects/meetingunderstanding/.
A Taxonomy of Specific Problem Classes in Text-to-Speech Synthesis: Comparing Commercial and Open Source Performance
Authors: Felix Burkhardt, Uwe D. Reichel
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Speech Synthesis, text-to-speech, Text to Speech, Text-To-Speech Synthesis

Current state-of-the-art speech synthesizers for domain-independent systems still struggle with the challenge of generating understandable and natural-sounding speech. This is mainly because the pronunciation of words of foreign origin, inflections and compound words often cannot be handled by rules. Furthermore there are too many of these for inclusion in exception dictionaries. We describe an approach to evaluating text-to-speech synthesizers with a subjective listening experiment. The focus is to differentiate between known problem classes for speech synthesizers. The target language is German but we believe that many of the described phenomena are not language specific. We distinguish the following problem categories: Normalization, Foreign linguistics, Natural writing, Language specific and General. Each of them is divided into five to three problem classes. Word lists for each of the above mentioned categories were compiled and synthesized by both a commercial and an open source synthesizer, both being based on the non-uniform unit-selection approach. The synthesized speech was evaluated by human judges using the Speechalyzer toolkit and the results are discussed. It shows that, as expected, the commercial synthesizer performs much better than the open-source one, and especially words of foreign origin were pronounced badly by both systems.
A Comparative Analysis of Crowdsourced Natural Language Corpora for Spoken Dialog Systems
Authors: Patricia Braunger, Hansj{\"o}rg Hofmann, Steffen Werner, Maria Schmidt
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), speech-recognition, Speech Recognition, valid

Recent spoken dialog systems have been able to recognize freely spoken user input in restricted domains thanks to statistical methods in the automatic speech recognition. These methods require a high number of natural language utterances to train the speech recognition engine and to assess the quality of the system. Since human speech offers many variants associated with a single intent, a high number of user utterances have to be elicited. Developers are therefore turning to crowdsourcing to collect this data. This paper compares three different methods to elicit multiple utterances for given semantics via crowd sourcing, namely with pictures, with text and with semantic entities. Specifically, we compare the methods with regard to the number of valid data and linguistic variance, whereby a quantitative and qualitative approach is proposed. In our study, the method with text led to a high variance in the utterances and a relatively low rate of invalid data.
A Singing Voice Database in Basque for Statistical Singing Synthesis of Bertsolaritza
Authors: Xabier Sarasola, Eva Navas, David Tavarez, Daniel Erro, Ibon Saratxaga, Inma Hernaez
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Singing Voice Synthesis

This paper describes the characteristics and structure of a Basque singing voice database of bertsolaritza. Bertsolaritza is a popular singing style from Basque Country sung exclusively in Basque that is improvised and a capella. The database is designed to be used in statistical singing voice synthesis for bertsolaritza style. Starting from the recordings and transcriptions of numerous singers, diarization and phoneme alignment experiments have been made to extract the singing voice from the recordings and create phoneme alignments. This labelling processes have been performed applying standard speech processing techniques and the results prove that these techniques can be used in this specific singing style.
AMISCO: The Austrian German Multi-Sensor Corpus
Authors: Hannes Pessentheiner, Thomas Pichler, Martin Hagm{\"u}ller
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We introduce a unique, comprehensive Austrian German multi-sensor corpus with moving and non-moving speakers to facilitate the evaluation of estimators and detectors that jointly detect a speaker{'}s spatial and temporal parameters. The corpus is suitable for various machine learning and signal processing tasks, linguistic studies, and studies related to a speaker{'}s fundamental frequency (due to recorded glottograms). Available corpora are limited to (synthetically generated/spatialized) speech data or recordings of musical instruments that lack moving speakers, glottograms, and/or multi-channel distant speech recordings. That is why we recorded 24 spatially non-moving and moving speakers, balanced male and female, to set up a two-room and 43-channel Austrian German multi-sensor speech corpus. It contains 8.2 hours of read speech based on phonetically balanced sentences, commands, and digits. The orthographic transcriptions include around 53,000 word tokens and 2,070 word types. Special features of this corpus are the laryngograph recordings (representing glottograms required to detect a speaker{'}s instantaneous fundamental frequency and pitch), corresponding clean-speech recordings, and spatial information and video data provided by four Kinects and a camera.
A Database of Laryngeal High-Speed Videos with Simultaneous High-Quality Audio Recordings of Pathological and Non-Pathological Voices
Authors: Philipp Aichinger, Immer Roesner, Matthias Leonhard, Doris-Maria Denk-Linnert, Wolfgang Bigenzahn, Berit Schneider-Stickler
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Vocal Bursts Intensity Prediction

Auditory voice quality judgements are used intensively for the clinical assessment of pathological voice. Voice quality concepts are fuzzily defined and poorly standardized however, which hinders scientific and clinical communication. The described database documents a wide variety of pathologies and is used to investigate auditory voice quality concepts with regard to phonation mechanisms. The database contains 375 laryngeal high-speed videos and simultaneous high-quality audio recordings of sustained phonations of 80 pathological and 40 non-pathological subjects. Interval wise annotations regarding video and audio quality, as well as voice quality ratings are provided. Video quality is annotated for the visibility of anatomical structures and artefacts such as blurring or reduced contrast. Voice quality annotations include ratings on the presence of dysphonia and diplophonia. The purpose of the database is to aid the formulation of observationally well-founded models of phonation and the development of model-based automatic detectors for distinct types of phonation, especially for clinically relevant nonmodal voice phenomena. Another application is the training of audio-based fundamental frequency extractors on video-based reference fundamental frequencies.
BulPhonC: Bulgarian Speech Corpus for the Development of ASR Technology
Authors: Neli Hateva, Petar Mitankin, Stoyan Mihov
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In this paper we introduce a Bulgarian speech database, which was created for the purpose of ASR technology development. The paper describes the design and the content of the speech database. We present also an empirical evaluation of the performance of a LVCSR system for Bulgarian trained on the BulPhonC data. The resource is available free for scientific usage.
Designing a Speech Corpus for the Development and Evaluation of Dictation Systems in Latvian
Authors: M{\=a}rcis Pinnis, Askars Salimbajevs, Ilze Auzi{\c{n}}a
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Data Augmentation, Language Modeling, Language Modelling, speech-recognition, Speech Recognition

In this paper the authors present a speech corpus designed and created for the development and evaluation of dictation systems in Latvian. The corpus consists of over nine hours of orthographically annotated speech from 30 different speakers. The corpus features spoken commands that are common for dictation systems for text editors. The corpus is evaluated in an automatic speech recognition scenario. Evaluation results in an ASR dictation scenario show that the addition of the corpus to the acoustic model training data in combination with language model adaptation allows to decrease the WER by up to relative 41.36{\%} (or 16.83{\%} in absolute numbers) compared to a baseline system without language model adaptation. Contribution of acoustic data augmentation is at relative 12.57{\%} (or 3.43{\%} absolute).
The LetsRead Corpus of Portuguese Children Reading Aloud for Performance Evaluation
Authors: Jorge Proen{\c{c}}a, Dirce Celorico, C, Sara eias, Carla Lopes, Fern Perdig{\~a}o, o
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

This paper introduces the LetsRead Corpus of European Portuguese read speech from 6 to 10 years old children. The motivation for the creation of this corpus stems from the inexistence of databases with recordings of reading tasks of Portuguese children with different performance levels and including all the common reading aloud disfluencies. It is also essential to develop techniques to fulfill the main objective of the LetsRead project: to automatically evaluate the reading performance of children through the analysis of reading tasks. The collected data amounts to 20 hours of speech from 284 children from private and public Portuguese schools, with each child carrying out two tasks: reading sentences and reading a list of pseudowords, both with varying levels of difficulty throughout the school grades. In this paper, the design of the reading tasks presented to children is described, as well as the collection procedure. Manually annotated data is analyzed according to disfluencies and reading performance. The considered word difficulty parameter is also confirmed to be suitable for the pseudoword reading tasks.
The BAS Speech Data Repository
Authors: Uwe Reichel, Florian Schiel, Thomas Kisler, Christoph Draxler, Nina P{\"o}rner
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

The BAS CLARIN speech data repository is introduced. At the current state it comprises 31 pre-dominantly German corpora of spoken language. It is compliant to the CLARIN-D as well as the OLAC requirements. This enables its embedding into several infrastructures. We give an overview over its structure, its implementation as well as the corpora it contains.
A Dutch Dysarthric Speech Database for Individualized Speech Therapy Research
Authors: Emre Yilmaz, Mario Ganzeboom, Lilian Beijer, Catia Cucchiarini, Helmer Strik
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Sentence, Speech-to-Text

We present a new Dutch dysarthric speech database containing utterances of neurological patients with Parkinson{'}s disease, traumatic brain injury and cerebrovascular accident. The speech content is phonetically and linguistically diversified by using numerous structured sentence and word lists. Containing more than 6 hours of mildly to moderately dysarthric speech, this database can be used for research on dysarthria and for developing and testing speech-to-text systems designed for medical applications. Current activities aimed at extending this database are also discussed.
A Publicly Available Indonesian Corpora for Automatic Abstractive and Extractive Chat Summarization
Authors: Fajri Koto
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

In this paper we report our effort to construct the first ever Indonesian corpora for chat summarization. Specifically, we utilized documents of multi-participant chat from a well known online instant messaging application, WhatsApp. We construct the gold standard by asking three native speakers to manually summarize 300 chat sections (152 of them contain images). As result, three reference summaries in extractive and either abstractive form are produced for each chat sections. The corpus is still in its early stage of investigation, yielding exciting possibilities of future works.
The OnForumS corpus from the Shared Task on Online Forum Summarisation at MultiLing 2015
Authors: Mijail Kabadjov, Udo Kruschwitz, Massimo Poesio, Josef Steinberger, Jorge Valderrama, Hugo Zaragoza
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Articles

In this paper we present the OnForumS corpus developed for the shared task of the same name on Online Forum Summarisation (OnForumS at MultiLing{'}15). The corpus consists of a set of news articles with associated readers{'} comments from The Guardian (English) and La Repubblica (Italian). It comes with four levels of annotation: argument structure, comment-article linking, sentiment and coreference. The former three were produced through crowdsourcing, whereas the latter, by an experienced annotator using a mature annotation scheme. Given its annotation breadth, we believe the corpus will prove a useful resource in stimulating and furthering research in the areas of Argumentation Mining, Summarisation, Sentiment, Coreference and the interlinks therein.
Automatic Enrichment of WordNet with Common-Sense Knowledge
Authors: Luigi Di Caro, Guido Boella
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Common Sense Reasoning, Word Sense Disambiguation

WordNet represents a cornerstone in the Computational Linguistics field, linking words to meanings (or senses) through a taxonomical representation of synsets, i.e., clusters of words with an equivalent meaning in a specific context often described by few definitions (or glosses) and examples. Most of the approaches to the Word Sense Disambiguation task fully rely on these short texts as a source of contextual information to match with the input text to disambiguate. This paper presents the first attempt to enrich synsets data with common-sense definitions, automatically retrieved from ConceptNet 5, and disambiguated accordingly to WordNet. The aim was to exploit the shared- and immediate-thinking nature of common-sense knowledge to extend the short but incredibly useful contextual information of the synsets. A manual evaluation on a subset of the entire result (which counts a total of almost 600K synset enrichments) shows a very high precision with an estimated good recall.
VPS-GradeUp: Graded Decisions on Usage Patterns
Authors: V{\'\i}t Baisa, Silvie Cinkov{\'a}, Ema Krej{\v{c}}ov{\'a}, Anna Vernerov{\'a}
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Clustering, LEMMA

We present VPS-GradeUp â€• a set of 11,400 graded human decisions on usage patterns of 29 English lexical verbs from the Pattern Dictionary of English Verbs by Patrick Hanks. The annotation contains, for each verb lemma, a batch of 50 concordances with the given lemma as KWIC, and for each of these concordances we provide a graded human decision on how well the individual PDEV patterns for this particular lemma illustrate the given concordance, indicated on a 7-point Likert scale for each PDEV pattern. With our annotation, we were pursuing a pilot investigation of the foundations of human clustering and disambiguation decisions with respect to usage patterns of verbs in context. The data set is publicly available at http://hdl.handle.net/11234/1-1585.
Sense-annotating a Lexical Substitution Data Set with Ubyline
Authors: Tristan Miller, Mohamed Khemakhem, Richard Eckart de Castilho, Iryna Gurevych
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We describe the construction of GLASS, a newly sense-annotated version of the German lexical substitution data set used at the GermEval 2015: LexSub shared task. Using the two annotation layers, we conduct the first known empirical study of the relationship between manually applied word senses and lexical substitutions. We find that synonymy and hypernymy/hyponymy are the only semantic relations directly linking targets to their substitutes, and that substitutes in the target{'}s hypernymy/hyponymy taxonomy closely align with the synonyms of a single GermaNet synset. Despite this, these substitutes account for a minority of those provided by the annotators. The results of our analysis accord with those of a previous study on English-language data (albeit with automatically induced word senses), leading us to suspect that the senseâ€•substitution relations we discovered may be of a universal nature. We also tentatively conclude that relatively cheap lexical substitution annotations can be used as a knowledge source for automatic WSD. Also introduced in this paper is Ubyline, the web application used to produce the sense annotations. Ubyline presents an intuitive user interface optimized for annotating lexical sample data, and is readily adaptable to sense inventories other than GermaNet.
A Corpus of Literal and Idiomatic Uses of German Infinitive-Verb Compounds
Authors: Andrea Horbach, Andrea Hensler, Sabine Krome, Jakob Prange, Werner Scholze-Stubenrecht, Diana Steffen, Stefan Thater, Christian Wellner, Manfred Pinkal
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Word Sense Disambiguation

We present an annotation study on a representative dataset of literal and idiomatic uses of German infinitive-verb compounds in newspaper and journal texts. Infinitive-verb compounds form a challenge for writers of German, because spelling regulations are different for literal and idiomatic uses. Through the participation of expert lexicographers we were able to obtain a high-quality corpus resource which offers itself as a testbed for automatic idiomaticity detection and coarse-grained word-sense disambiguation. We trained a classifier on the corpus which was able to distinguish literal and idiomatic uses with an accuracy of 85 {\%}.
The SemDaX Corpus â€• Sense Annotations with Scalable Sense Inventories
Authors: Bolette Pedersen, Anna Braasch, Anders Johannsen, H{\'e}ctor Mart{\'\i}nez Alonso, Sanni Nimb, Sussi Olsen, Anders S{\o}gaard, Nicolai Hartvig S{\o}rensen
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5

We launch the SemDaX corpus which is a recently completed Danish human-annotated corpus available through a CLARIN academic license. The corpus includes approx. 90,000 words, comprises six textual domains, and is annotated with sense inventories of different granularity. The aim of the developed corpus is twofold: i) to assess the reliability of the different sense annotation schemes for Danish measured by qualitative analyses and annotation agreement scores, and ii) to serve as training and test data for machine learning algorithms with the practical purpose of developing sense taggers for Danish. To these aims, we take a new approach to human-annotated corpus resources by double annotating a much larger part of the corpus than what is normally seen: for the all-words task we double annotated 60{\%} of the material and for the lexical sample task 100{\%}. We include in the corpus not only the adjucated files, but also the diverging annotations. In other words, we consider not all disagreement to be noise, but rather to contain valuable linguistic information that can help us improve our annotation schemes and our learning algorithms.
Graded and Word-Sense-Disambiguation Decisions in Corpus Pattern Analysis: a Pilot Study
Authors: Silvie Cinkov{\'a}, Ema Krej{\v{c}}ov{\'a}, Anna Vernerov{\'a}, V{\'\i}t Baisa
Date: 2016-05-01 00:00:00
Proceeding: LREC 2016 5
Tasks: Word Sense Disambiguation

We present a pilot analysis of a new linguistic resource, VPS-GradeUp (available at http://hdl.handle.net/11234/1-1585). The resource contains 11,400 graded human decisions on usage patterns of 29 English lexical verbs, randomly selected from the Pattern Dictionary of English Verbs (Hanks, 2000 2014) based on their frequency and the number of senses their lemmas have in PDEV. This data set has been created to observe the interannotator agreement on PDEV patterns produced using the Corpus Pattern Analysis (Hanks, 2013). Apart from the graded decisions, the data set also contains traditional Word-Sense-Disambiguation (WSD) labels. We analyze the associations between the graded annotation and WSD annotation. The results of the respective annotations do not correlate with the size of the usage pattern inventory for the respective verbs lemmas, which makes the data set worth further linguistic analysis.