Dynamic Extension of ASR Lexicon Using Wikipedia Data

International audienceDespite recent progress in developing Large Vocabulary Continuous Speech Recognition Systems (LVCSR), these systems suffer from Out-Of-Vocabulary words (OOV). In many cases, the OOV words are Proper Nouns (PNs). The correct recognition of PNs is essential for broadcast news, audio indexing, etc. In this article, we address the problem of OOV PN retrieval in the framework of broadcast news LVCSR. We focused on dynamic (document dependent) extension of LVCSR lexicon. To retrieve relevant OOV PNs, we propose to use a very large multipurpose text corpus: Wikipedia. This corpus contains a huge number of PNs. These PNs are grouped in semantically similar classes using word embedding. We use a two-step approach: first, we select OOV PN pertinent classes with a multi-class Deep Neural Network (DNN). Secondly, we rank the OOVs of the selected classes. The experiments on French broadcast news show that the Bi-GRU model outperforms other studied models. Speech recognition experiments demonstrate the effectiveness of the proposed methodology

RNN Language Model Estimation for Out-of-Vocabulary Words

International audienceOne important issue of speech recognition systems is Out-of Vocabulary words (OOV). These words, often proper nouns or new words, are essential for documents to be transcribed correctly. Thus, they must be integrated in the language model (LM) and the lexicon of the speech recognition system. This article proposes new approaches to OOV proper noun probability estimation using Recurrent Neural Network Language Model (RNNLM). The proposed approaches are based on the notion of closest in-vocabulary (IV) words (list of brothers) to a given OOV proper noun. The probabilities of these words are used to estimate the probabilities of OOV proper nouns thanks to RNNLM. Three methods for retrieving the relevant list of brothers are studied. The main advantages of the proposed approaches are that the RNNLM is not retrained and the architecture of the RNNLM is kept intact. Experiments on real text data from the website of the Euronews channel show relative perplexity reductions of about 14% compared to baseline RNNLM

Towards a unified model for speech and language processing

Ce travail de recherche explore les méthodes d’apprentissage profond de la parole et du langage, y inclus la reconnaissance et la synthèse de la parole, la conversion des graphèmes en phonèmes et vice-versa, les modèles génératifs, visant de reformuler des tâches spécifiques dans un problème plus général de trouver une représentation universelle d’information contenue dans chaque modalité et de transférer un signal d’une modalité à une autre en se servant de telles représentations universelles et à générer des représentations dans plusieurs modalités. Il est compris de deux projets de recherche: 1) SoundChoice, un modèle graphème-phonème tenant compte du contexte au niveau de la phrase qui réalise de bonnes performances et des améliorations remarquables comparativement à un modèle de base et 2) MAdmixture, une nouvelle approche pour apprendre des représentations multimodales dans un espace latent commun.The present work explores the use of deep learning methods applied to a variety of areas in speech and language processing including speech recognition, grapheme-to-phoneme conversion, speech synthesis, generative models for speech and others to build toward a unified approach that reframes these individual tasks into a more general problem of finding a universal representation of information encoded in different modalities and being able to seamlessly transfer a signal from one modality to another by converting it to this universal representations and to generate samples in multiple modalities. It consists of two main research projects: 1) SoundChocice, a context-aware sentence level Grapheme-to-Phoneme model achieving solid performance on the task and a significant improvement on phoneme disambiguation over baseline models and 2) MAdmixture, a novel approach to learning a variety of speech representations in a common latent space

Morphologically motivated word classes for very large vocabulary speech recognition of Finnish and Estonian

We study class-based n-gram and neural network language models for very large vocabulary speech recognition of two morphologically rich languages: Finnish and Estonian. Due to morphological processes such as derivation, inflection and compounding, the models need to be trained with vocabulary sizes of several millions of word types. Class-based language modelling is in this case a powerful approach to alleviate the data sparsity and reduce the computational load. For a very large vocabulary, bigram statistics may not be an optimal way to derive the classes. We thus study utilizing the output of a morphological analyzer to achieve efficient word classes. We show that efficient classes can be learned by refining the morphological classes to smaller equivalence classes using merging, splitting and exchange procedures with suitable constraints. This type of classification can improve the results, particularly when language model training data is not very large. We also extend the previous analyses by rescoring the hypotheses obtained from a very large vocabulary recognizer using class-based neural network language models. We show that despite the fixed vocabulary, carefully constructed classes for word-based language models can in some cases result in lower error rates than subword-based unlimited vocabulary language models.We study class-based n-gram and neural network language models for very large vocabulary speech recognition of two morphologically rich languages: Finnish and Estonian. Due to morphological processes such as derivation, inflection and compounding, the models need to be trained with vocabulary sizes of several millions of word types. Class-based language modelling is in this case a powerful approach to alleviate the data sparsity and reduce the computational load. For a very large vocabulary, bigram statistics may not be an optimal way to derive the classes. We thus study utilizing the output of a morphological analyzer to achieve efficient word classes. We show that efficient classes can be learned by refining the morphological classes to smaller equivalence classes using merging, splitting and exchange procedures with suitable constraints. This type of classification can improve the results, particularly when language model training data is not very large. We also extend the previous analyses by rescoring the hypotheses obtained from a very large vocabulary recognizer using class-based neural network language models. We show that despite the fixed vocabulary, carefully constructed classes for word-based language models can in some cases result in lower error rates than subword-based unlimited vocabulary language models.Peer reviewe

An approach to provide dynamic, illustrative, video-based guidance within a goal-driven smart home

The global population is aging in a never-before seen way, introducing an increasing ageing-related cognitive ailments, such as dementia. This aging is coupled with a reduction in the global support ratio, reducing the availability of formal and informal support and therefore capacity to care for those suffering these aging related ailments. Assistive Smart Homes (SH) are a promising form of technology enabling assistance with activities of daily living, providing support of suffers of cognitive ailments and increasing their independence and quality of life. Traditional SH systems have deficiencies that have been partially addressed by through goal-driven SH systems. Goal-driven SHs incorporate flexible activity models, goals, which partially address some of these issues. Goals may be combined to provide assistance with dynamic and variable activities. This paradigm-shift, however, introduces the need to provide dynamic assistance within such SHs. This study presents a novel approach to achieve this through video based content analysis and a mechanism to facilitate matching analysed videos to dynamic activities/goals. The mechanism behind this approach is detailed and followed by the presentation of an evaluation where showing promising results were shown

Artificial Neural Network methods applied to sentiment analysis

Sentiment Analysis (SA) is the study of opinions and emotions that are conveyed by text. This field of study has commercial applications for example in market research (e.g., “What do customers like and dislike about a product?”) and consumer behavior (e.g., “Which book will a customer buy next when he wrote a positive review about book X?”). A private person can benefit from SA by automatic movie or restaurant recommendations, or from applications on the computer or smart phone that adapt to the user’s current mood. In this thesis we will put forward research on artificial Neural Network (NN) methods applied to SA. Many challenges arise, such as sarcasm, domain dependency, and data scarcity, that need to be addressed by a successful system. In the first part of this thesis we perform linguistic analysis of a word (“hard”) under the light of SA. We show that sentiment-specific word sense disambiguation is necessary to distinguish fine nuances of polarity. Commonly available resources are not sufficient for this. The introduced Contextually Enhanced Sentiment Lexicon (CESL) is used to label occurrences of “hard” in a real dataset with its sense. That allows us to train a Support Vector Machine (SVM) with deep learning features that predicts the polarity of a single occurrence of the word, just given its context words. We show that the features we propose improve the result compared to existing standard features. Since the labeling effort is not negligible, we propose a clustering approach that reduces the manual effort to a minimum. The deep learning features that help predicting fine-grained, context-dependent polarity are computed by a Neural Network Language Model (NNLM), namely a variant of the Log-Bilinear Language model (LBL). By improving this model the performance of polarity classification might as well improve. Thus, we propose a non-linear version of the LBL and the vectorized Log-Bilinear Language model (vLBL), because non-linear models are generally considered more powerful. In a parameter study on a language modeling task, we show that the non-linear versions indeed perform better than their linear counterparts. However, the difference is small, except for settings where the model has only few parameters, which might be the case when little training data is available and the model therefore needs to be smaller in order to avoid overfitting. An alternative approach to fine-grained polarity classification as used above is to train classifiers that will do the distinction automatically. Due to the complexity of the task, the challenges of SA in general, and certain domain-specific issues (e.g., when using Twitter text) existing systems have much room to improve. Often statistical classifiers are used with simple Bag-of-Words (BOW) features or count features that stem from sentiment lexicons. We introduce a linguistically-informed Convolutional Neural Network (lingCNN) that builds upon the fact that there has been much research on language in general and sentiment lexicons in particular. lingCNN makes use of two types of linguistic features: word-based and sentence-based. Word-based features comprise features derived from sentiment lexicons, such as polarity or valence and general knowledge about language, such as a negation-based feature. Sentence-based features are also based on lexicon counts and valences. The combination of both types of features is superior to the original model without these features. Especially, when little training data is available (that can be the case for different languages that are underresourced), lingCNN proves to be significantly better (up to 12 macro-F1 points). Although, linguistic features in terms of sentiment lexicons are beneficial, their usage gives rise to a new set of problems. Most lexicons consist of infinitive forms of words only. Especially, lexicons for low-resource languages. However, the text that needs to be classified is unnormalized. Hence, we want to answer the question if morphological information is necessary for SA or if a system that neglects all this information and therefore can make better use of lexicons actually has an advantage. Our approach is to first stem or lemmatize a dataset and then perform polarity classification on it. On Czech and English datasets we show that better results can be achieved with normalization. As a positive side effect, we can compute better word embeddings by first normalizing the training corpus. This works especially well for languages that have rich morphology. We show on word similarity datasets for English, German, and Spanish that our embeddings improve performance. On a new WordNet-based evaluation we confirm these results on five different languages (Czech, English, German, Hungarian, and Spanish). The benefit of this new evaluation is further that it can be used for many other languages, as the only resource that is required is a WordNet. In the last part of the thesis, we use a recently introduced method to create an ultradense sentiment space out of generic word embeddings. This method allows us to compress 400 dimensional word embeddings down to 40 or even just 4 dimensions and still get similar results on a polarity classification task. While the training speed increases by a factor of 44, the difference in classification performance is not significant.Sentiment Analyse (SA) ist das Untersuchen von Meinungen und Emotionen die durch Text übermittelt werden. Dieses Forschungsgebiet findet kommerzielle Anwendungen in Marktforschung (z.B.: „Was mögen Kunden an einem Produkt (nicht)?“) und Konsumentenverhalten (z.B.: „Welches Buch wird ein Kunde als nächstes kaufen, nachdem er eine positive Rezension über Buch X geschrieben hat?“). Aber auch als Privatperson kann man von Forschung in SA profitieren. Beispiele hierfür sind automatisch erstellte Film- oder Restaurantempfehlungen oder Anwendungen auf Computer oder Smartphone die sich der aktuellen Stimmungslage des Benutzers anpassen. In dieser Arbeit werden wir Forschung auf dem Gebiet der Neuronen Netze (NN) angewendet auf SA vorantreiben. Dabei ergeben sich viele Herausforderungen, wie Sarkasmus, Domänenabhängigkeit und Datenarmut, die ein erfolgreiches System angehen muss. Im ersten Teil der Arbeit führen wir eine linguistische Analyse des englischen Wortes „hard“ in Hinblick auf SA durch. Wir zeigen, dass sentiment-spezifische Wortbedeutungsdisambiguierung notwendig ist, um feine Nuancen von Polarität (positive vs. negative Stimmung) unterscheiden zu können. Häufig verwendete, frei verfügbare Ressourcen sind dafür nicht ausreichend. Daher stellen wir CESL (Contextually Enhanced Sentiment Lexicon), ein sentiment-spezifisches Bedeutungslexicon vor, welches verwendet wird, um Vorkommen von „hard“ in einem realen Datensatz mit seinen Bedeutungen zu versehen. Das Lexikon erlaubt es eine Support Vector Machine (SVM) mit Features aus dem Deep Learning zu trainieren, die in der Lage ist, die Polarität eines Vorkommens nur anhand seiner Kontextwörter vorherzusagen. Wir zeigen, dass die vorgestellten Features die Ergebnisse der SVM verglichen mit Standard-Features verbessern. Da der Aufwand für das Erstellen von markierten Trainingsdaten nicht zu unterschätzen ist, stellen wir einen Clustering-Ansatz vor, der den manuellen Markierungsaufwand auf ein Minimum reduziert. Die Deep Learning Features, die die Vorhersage von feingranularer, kontextabhängiger Polarität verbessern, werden mittels eines neuronalen Sprachmodells, genauer eines Log-Bilinear Language model (LBL)s, berechnet. Wenn man dieses Modell verbessert, wird vermutlich auch das Ergebnis der Polaritätsklassifikation verbessert. Daher führen wir nichtlineare Versionen des LBL und vectorized Log-Bilinear Language model (vLBL) ein, weil nichtlineare Modelle generell als mächtiger angesehen werden. In einer Parameterstudie zur Sprachmodellierung zeigen wir, dass nichtlineare Modelle tatsächlich besser abschneiden, als ihre linearen Gegenstücke. Allerdings ist der Unterschied gering, es sei denn die Modelle können nur auf wenige Parameter zurückgreifen. So etwas kommt zum Beispiel vor, wenn nur wenige Trainingsdaten verfügbar sind und das Modell deshalb kleiner sein muss, um Überanpassung zu verhindern. Ein alternativer Ansatz zur feingranularen Polaritätsklassifikation wie oben verwendet, ist es, einen Klassifikator zu trainieren, der die Unterscheidung automatisch vornimmt. Durch die Komplexität der Aufgabe, der Herausforderungen von SA im Allgemeinen und speziellen domänenspezifischen Problemen (z.B.: wenn Twitter-Daten verwendet werden) haben existierende Systeme noch immer großes Optimierungspotential. Oftmals verwenden statistische Klassifikatoren einfache Bag-of-Words (BOW)-Features. Alternativ kommen Zähl-Features zum Einsatz, die auf Sentiment-Lexika aufsetzen. Wir stellen linguistically-informed Convolutional Neural Network (lingCNN) vor, dass auf dem Fakt beruht, dass bereits viel Forschung in Sprachen und Sentiment-Lexika geflossen ist. lingCNN macht von zwei linguistischen Feature-Typen Gebrauch: wortbasierte und satzbasierte. Wort-basierte Features umfassen Features die von Sentiment-Lexika, wie Polarität oder Valenz (die Stärke der Polarität) und generellem Wissen über Sprache, z.B.: Verneinung, herrühren. Satzbasierte Features basieren ebenfalls auf Zähl-Features von Lexika und auf Valenzen. Die Kombination beider Feature-Typen ist dem Originalmodell ohne linguistische Features überlegen. Besonders wenn wenige Trainingsdatensätze vorhanden sind (das kann der Fall für Sprachen sein, die weniger erforscht sind als englisch). lingCNN schneidet signifikant besser ab (bis zu 12 macro-F1 Punkte). Obwohl linguistische Features basierend auf Sentiment-Lexika vorteilhaft sind, führt deren Verwendung zu neuen Problemen. Der Großteil der Lexika enthält nur Infinitivformen der Wörter. Dies gilt insbesondere für Sprachen mit wenigen Ressourcen. Das ist eine Herausforderung, weil der Text der klassifiziert werden soll in der Regel nicht normalisiert ist. Daher wollen wir die Frage beantworten, ob morphologische Information für SA überhaupt notwendig ist oder ob ein System, dass jegliche morphologische Information ignoriert und dadurch bessere Verwendung der Lexika erzielt, einen Vorteil genießt. Unser Ansatz besteht aus Stemming und Lemmatisierung des Datensatzes, bevor dann die Polaritätsklassifikation durchgeführt wird. Auf englischen und tschechischen Daten zeigen wir, dass durch Normalisierung bessere Ergebnisse erzielt werden. Als positiven Nebeneffekt kann man bessere Wortrepresentationen (engl. word embeddings) berechnen, indem das Trainingskorpus zuerst normalisiert wird. Das funktioniert besonders gut für morphologisch reiche Sprachen. Wir zeigen auf Datensätzen zur Wortähnlichkeit für deutsch, englisch und spanisch, dass unsere Wortrepresentationen die Ergebnisse verbessern. In einer neuen WordNet-basierten Evaluation bestätigen wir diese Ergebnisse für fünf verschiedene Sprachen (deutsch, englisch, spanisch, tschechisch und ungarisch). Der Vorteil dieser Evaluation ist weiterhin, dass sie für viele Sprachen angewendet werden kann, weil sie lediglich ein WordNet als Ressource benötigt. Im letzten Teil der Arbeit verwenden wir eine kürzlich vorgestellte Methode zur Erstellen eines ultradichten Sentiment-Raumes aus generischen Wortrepresentationen. Diese Methode erlaubt es uns 400 dimensionale Wortrepresentationen auf 40 oder sogar nur 4 Dimensionen zu komprimieren und weiterhin die gleichen Resultate in Polaritätsklassifikation zu erhalten. Während die Trainingsgeschwindigkeit um einen Faktor von 44 verbessert wird, sind die Unterschiede in der Polaritätsklassifikation nicht signifikant