Topic dependent language modelling for spoken term detection

This paper investigates the effect of topic dependent language models (TDLM) on phonetic spoken term detection (STD) using dynamic match lattice spotting (DMLS). Phonetic STD consists of two steps: indexing and search. The accuracy of indexing audio segments into phone sequences using phone recognition methods directly affects the accuracy of the final STD system. If the topic of a document in known, recognizing the spoken words and indexing them to an intermediate representation is an easier task and consequently, detecting a search word in it will be more accurate and robust. In this paper, we propose the use of TDLMs in the indexing stage to improve the accuracy of STD in situations where the topic of the audio document is known in advance. It is shown that using TDLMs instead of the traditional general language model (GLM) improves STD performance according to figure of merit (FOM) criteria.

Medical semantic similarity with a neural language model

Advances in neural network language models have demonstrated that these models can effectively learn representations of words meaning. In this paper, we explore a variation of neural language models that can learn on concepts taken from structured ontologies and extracted from free-text, rather than directly from terms in free-text. This model is employed for the task of measuring semantic similarity between medical concepts, a task that is central to a number of techniques in medical informatics and information retrieval. The model is built with two medical corpora (journal abstracts and patient records) and empirically validated on two ground-truth datasets of human-judged concept pairs assessed by medical professionals. Empirically, our approach correlates closely with expert human assessors ($\approx$ 0.9) and outperforms a number of state-of-the-art benchmarks for medical semantic similarity. The demonstrated superiority of this model for providing an effective semantic similarity measure is promising in that this may translate into effectiveness gains for techniques in medical information retrieval and medical informatics (e.g., query expansion and literature-based discovery).

How everyday language can and will boost effective information retrieval

Typing 2 or 3 keywords into a browser has become an easy and efficient way to find information. Yet, typing even short queries becomes tedious on ever shrinking (virtual) keyboards. Meanwhile, speech processing is maturing rapidly, facilitating everyday language input. Also, wearable technology can inform users proactively by listening in on their conversations or processing their social media interactions. Given these developments, everyday language may soon become the new input of choice. We present an information retrieval (IR) algorithm specifically designed to accept everyday language. It integrates two paradigms of information retrieval, previously studied in isolation; one directed mainly at the surface structure of language, the other primarily at the underlying meaning. The integration was achieved by a Markov machine that encodes meaning by its transition graph, and surface structure by the language it generates. A rigorous evaluation of the approach showed, first, that it can compete with the quality of existing language models, second, that it is more effective the more verbose the input, and third, as a consequence, that it is promising for an imminent transition from keyword input, where the onus is on the user to formulate concise queries, to a modality where users can express more freely, more informal, and more natural their need for information in everyday language.

Developing and testing readability measurements for second language learners

This research constructed a readability measurement for French speakers who view English as a second language. It identified the true cognates, which are the similar words from these two languages, as an indicator of the difficulty of an English text for French people. A multilingual lexical resource is used to detect true cognates in text, and Statistical Language Modelling to predict the predict the readability level. The proposed enhanced statistical language model is making a step in the right direction by improving the accuracy of readability predictions for French speakers by up to 10% compared to state of the art approaches. The outcome of this study could accelerate the learning process for French speakers who are studying English. More importantly, this study also benefits the readability estimation research community, presenting an approach and evaluation at sentence level as well as innovating with the use of cognates as a new text feature.

Swedish advertising language in Finland mirrored by the newspaper advertisements of the Stockmanns department store during the 20th century

The advertising business is often said to favour a modern, innovative language use. This is a statement not easily verified. Newspaper ads are in fact the genre of written language that linguists have paid the least attention to. People writing texts for newspaper ads are individuals representing contemporary language use. Advertisements representing different periods therefore diverge not only regarding the change of style and form advertising undergoes over time, but changes in the language itself also reflect the continuous process of alteration in a speech community. Advertisements and marketing material on the whole, are also read by many individuals who otherwise are not accustomed to reading at all. The marketing manager, the copywriter and the Art Director, in other words, produce texts that unconsciously function as language models. Changes are not created by, or urged on by linguistic expertise, but by ordinary users confronting other ordinary users. From a sociolinguistic perspective the widely diffused advertising language is therefore a most influential factor.

Reklamsvenska i Finland speglad genom varuhuset Stockmanns tidningsannonser under det 20e seklet - Swedish Advertising Language in Finland Mirrored by the Newspaper Advertisements of the Stockmanns Department Store during the 20th Century

Reklam sägs använda ett modernt, gärna ett nyskapande språk. Detta är ett påstående som inte så lätt kan verifieras. Tidningsannonsen är troligen den skriftspråksgenre som har fått minst uppmärksamhet av språkforskare. De som skriver texten i en tidningsannons är personer som representerar det samtida språkbruket. Annonser som representerar olika tidsepoker skiljer sig därför från varandra inte bara genom att annonsen förändras i fråga om stil och form. Annonsens språk avspeglar också den språkliga förändringsprocess som kontinuerligt pågår i varje språksamhälle. Annonser, och marknadsföringsmaterial över huvud taget, läses också av många människor som i övrigt läser mycket litet eller kanske inte alls. Marknadsföraren, reklamskribenten (copywriter) och AD:n producerar m.a.o. texter som på ett omedvetet sätt kommer att vara språkmodeller för sina läsare. Förändringar i språket kreeras inte och drivs inte på av språkforskare, utan av vanliga språkbrukare i interaktion med andra språkbrukare. Sett ur ett sociolingvistiskt perspektiv har det vitt spridda reklamspråket därför inflytande på språket i samhället. Syftet med det reklamspråksprojekt som presenteras i föreliggande rapport är att analysera hur och när förändringar i svenskan som uppträder i Sverige dyker upp i annonser som skrivs på svenska i Finland. Reklam på svenska Finland under 1900-talet står i fokus, och tidningsannonser för Stockmanns varuhus i Helsingfors utgör primärmaterialet. Tidningsannonser för varuhuset Nordiska Kompaniet (NK) i Stockholm under motsvarande tid tjänar som jämförelsematerial. I denna rapport presenteras projektets syfte, de uppställda forskningsfrågorna, och resonemanget illustreras med exempel ur projektmaterialet. Rapporten innehåller också en beskrivning av projektets reklamdatabas och basfakta om material och metoder. -

FIN-CLARIN: A Framework for Depositing and Disseminating Language Resources for R&D

Researchers and developers in academia and industry would benefit from a facility that enables them to easily locate, licence and use the kind of empirical data they need for testing and refining their hypotheses and to deposit and disseminate their data e.g. to support replication and validation of reported scientific experiments. To answer these needs initially in Finland, there is an ongoing project at University of Helsinki and its collaborators to create a user-friendly web service for researchers and developers in Finland and other countries. In our talk, we describe ongoing work to create a palette of extensive but easily available Finnish language resources and technologies for the research community, including lexical resources, wordnets, morphologically tagged corpora, dependency syntactic treebanks and parsebanks, open-source finite state toolkits and libraries and language models to support text analysis and processing at customer site. Also first publicly available results are presented.

Unrestricted Kannada online handwritten akshara recognition using SDTW

In this paper, we present an unrestricted Kannada online handwritten character recognizer which is viable for real time applications. It handles Kannada and Indo-Arabic numerals, punctuation marks and special symbols like $, &, # etc, apart from all the aksharas of the Kannada script. The dataset used has handwriting of 69 people from four different locations, making the recognition writer independent. It was found that for the DTW classifier, using smoothed first derivatives as features, enhanced the performance to 89% as compared to preprocessed co-ordinates which gave 85%, but was too inefficient in terms of time. To overcome this, we used Statistical Dynamic Time Warping (SDTW) and achieved 46 times faster classification with comparable accuracy i.e. 88%, making it fast enough for practical applications. The accuracies reported are raw symbol recognition results from the classifier. Thus, there is good scope of improvement in actual applications. Where domain constraints such as fixed vocabulary, language models and post processing can be employed. A working demo is also available on tablet PC for recognition of Kannada words.

Discriminative language model adaptation for Mandarin broadcast speech transcription and translation

This paper investigates unsupervised test-time adaptation of language models (LM) using discriminative methods for a Mandarin broadcast speech transcription and translation task. A standard approach to adapt interpolated language models to is to optimize the component weights by minimizing the perplexity on supervision data. This is a widely made approximation for language modeling in automatic speech recognition (ASR) systems. For speech translation tasks, it is unclear whether a strong correlation still exists between perplexity and various forms of error cost functions in recognition and translation stages. The proposed minimum Bayes risk (MBR) based approach provides a flexible framework for unsupervised LM adaptation. It generalizes to a variety of forms of recognition and translation error metrics. LM adaptation is performed at the audio document level using either the character error rate (CER), or translation edit rate (TER) as the cost function. An efficient parameter estimation scheme using the extended Baum-Welch (EBW) algorithm is proposed. Experimental results on a state-of-the-art speech recognition and translation system are presented. The MBR adapted language models gave the best recognition and translation performance and reduced the TER score by up to 0.54% absolute. © 2007 IEEE.

Language model cross adaptation for LVCSR system combination

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple subsystems developed at different sites. Cross system adaptation can be used as an alternative to direct hypothesis level combination schemes such as ROVER. In normal cross adaptation it is assumed that useful diversity among systems exists only at acoustic level. However, complimentary features among complex LVCSR systems also manifest themselves in other layers of modelling hierarchy, e.g., subword and word level. It is thus interesting to also cross adapt language models (LM) to capture them. In this paper cross adaptation of multi-level LMs modelling both syllable and word sequences was investigated to improve LVCSR system combination. Significant error rate gains up to 6.7% rel. were obtained over ROVER and acoustic model only cross adaptation when combining 13 Chinese LVCSR subsystems used in the 2010 DARPA GALE evaluation. © 2010 ISCA.

Use of contexts in language model interpolation and adaptation

Language models (LMs) are often constructed by building multiple individual component models that are combined using context independent interpolation weights. By tuning these weights, using either perplexity or discriminative approaches, it is possible to adapt LMs to a particular task. This paper investigates the use of context dependent weighting in both interpolation and test-time adaptation of language models. Depending on the previous word contexts, a discrete history weighting function is used to adjust the contribution from each component model. As this dramatically increases the number of parameters to estimate, robust weight estimation schemes are required. Several approaches are described in this paper. The first approach is based on MAP estimation where interpolation weights of lower order contexts are used as smoothing priors. The second approach uses training data to ensure robust estimation of LM interpolation weights. This can also serve as a smoothing prior for MAP adaptation. A normalized perplexity metric is proposed to handle the bias of the standard perplexity criterion to corpus size. A range of schemes to combine weight information obtained from training data and test data hypotheses are also proposed to improve robustness during context dependent LM adaptation. In addition, a minimum Bayes' risk (MBR) based discriminative training scheme is also proposed. An efficient weighted finite state transducer (WFST) decoding algorithm for context dependent interpolation is also presented. The proposed technique was evaluated using a state-of-the-art Mandarin Chinese broadcast speech transcription task. Character error rate (CER) reductions up to 7.3 relative were obtained as well as consistent perplexity improvements. © 2012 Elsevier Ltd. All rights reserved.

Language model cross adaptation for LVCSR system combination

State-of-the-art large vocabulary continuous speech recognition (LVCSR) systems often combine outputs from multiple sub-systems that may even be developed at different sites. Cross system adaptation, in which model adaptation is performed using the outputs from another sub-system, can be used as an alternative to hypothesis level combination schemes such as ROVER. Normally cross adaptation is only performed on the acoustic models. However, there are many other levels in LVCSR systems' modelling hierarchy where complimentary features may be exploited, for example, the sub-word and the word level, to further improve cross adaptation based system combination. It is thus interesting to also cross adapt language models (LMs) to capture these additional useful features. In this paper cross adaptation is applied to three forms of language models, a multi-level LM that models both syllable and word sequences, a word level neural network LM, and the linear combination of the two. Significant error rate reductions of 4.0-7.1% relative were obtained over ROVER and acoustic model only cross adaptation when combining a range of Chinese LVCSR sub-systems used in the 2010 and 2011 DARPA GALE evaluations. © 2012 Elsevier Ltd. All rights reserved.

Exploratory study : exploring the effectiveness of a literacy-based intervention with children with language impairments

Research points clearly to the need for all concerned stakeholders to adopt a preventative approach while intervening with children who are at-risk for future reading disabilities. Research has indicated also that a particular sub-group of children at-risk for reading impairments include preschool children with language impairments (Catts, 1993). Preschool children with language impairments may have difficulties with emergent literacy skills - important prerequisite skills necessary for successful formal reading. Only in the past decade have researchers begun to study the effects of emergent literacy intervention on preschool children with language impairments. As such, the current study continues this investigation of how to effectively implement an emergent literacy therapy aimed at supporting preschool children with language impairments. In addition to this, the current study explores emergent literacy intervention within an applied clinical setting. The setting, presents a host of methodological and theoretical challenges - challenges that will advance the field of understanding children within naturalistic settings. This exploratory study included thirty-eight participants who were recruited from Speech Services Niagara, a local preschool speech and language program. Using a between-group pre- and posttest design, this study compared two intervention approaches - an experimental emergent literacy intervention and a traditional language intervention. The experimental intervention was adopted from Read It Again! (Justice, McGinty, Beckman, & Kilday, 2006) and the traditional language intervention was based on the traditional models of language therapy typically used in preschool speech and language models across Ontario. 5 Results indicated that the emergent literacy intervention was superior to the ,t..3>~, ~\., ;./h traditional language therapy in improving the children's alphabet knowledge, print and word awareness and phonological awareness. Moreover, results revealed that children with more severe language impairments require greater support and more explicit instruction than children with moderate language impairments. Another important finding indicated that the effects of the preschool emergent literacy intervention used in this study may not be sustainable as children enter grade one. The implications of this study point to the need to support preschool children with language impairments with intensive emergent literacy intervention that extends beyond preschool into formal educational settings.

Intégration du contexte en traduction statistique à l’aide d’un perceptron à plusieurs couches

Les systèmes de traduction statistique à base de segments traduisent les phrases un segment à la fois, en plusieurs étapes. À chaque étape, ces systèmes ne considèrent que très peu d’informations pour choisir la traduction d’un segment. Les scores du dictionnaire de segments bilingues sont calculés sans égard aux contextes dans lesquels ils sont utilisés et les modèles de langue ne considèrent que les quelques mots entourant le segment traduit.Dans cette thèse, nous proposons un nouveau modèle considérant la phrase en entier lors de la sélection de chaque mot cible. Notre modèle d’intégration du contexte se différentie des précédents par l’utilisation d’un ppc (perceptron à plusieurs couches). Une propriété intéressante des ppc est leur couche cachée, qui propose une représentation alternative à celle offerte par les mots pour encoder les phrases à traduire. Une évaluation superficielle de cette représentation alter- native nous a montré qu’elle est capable de regrouper certaines phrases sources similaires même si elles étaient formulées différemment. Nous avons d’abord comparé avantageusement les prédictions de nos ppc à celles d’ibm1, un modèle couramment utilisé en traduction. Nous avons ensuite intégré nos ppc à notre système de traduction statistique de l’anglais vers le français. Nos ppc ont amélioré les traductions de notre système de base et d’un deuxième système de référence auquel était intégré IBM1.