21 research outputs found

    Speech Recognition

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    Chapters in the first part of the book cover all the essential speech processing techniques for building robust, automatic speech recognition systems: the representation for speech signals and the methods for speech-features extraction, acoustic and language modeling, efficient algorithms for searching the hypothesis space, and multimodal approaches to speech recognition. The last part of the book is devoted to other speech processing applications that can use the information from automatic speech recognition for speaker identification and tracking, for prosody modeling in emotion-detection systems and in other speech processing applications that are able to operate in real-world environments, like mobile communication services and smart homes

    CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

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    Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

    Lattice-based statistical spoken document retrieval

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    Ph.DDOCTOR OF PHILOSOPH

    24th Nordic Conference on Computational Linguistics (NoDaLiDa)

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    Essential Speech and Language Technology for Dutch: Results by the STEVIN-programme

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    Computational Linguistics; Germanic Languages; Artificial Intelligence (incl. Robotics); Computing Methodologie

    Towards structured neural spoken dialogue modelling.

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    195 p.In this thesis, we try to alleviate some of the weaknesses of the current approaches to dialogue modelling,one of the most challenging areas of Artificial Intelligence. We target three different types of dialogues(open-domain, task-oriented and coaching sessions), and use mainly machine learning algorithms to traindialogue models. One challenge of open-domain chatbots is their lack of response variety, which can betackled using Generative Adversarial Networks (GANs). We present two methodological contributions inthis regard. On the one hand, we develop a method to circumvent the non-differentiability of textprocessingGANs. On the other hand, we extend the conventional task of discriminators, which oftenoperate at a single response level, to the batch level. Meanwhile, two crucial aspects of task-orientedsystems are their understanding capabilities because they need to correctly interpret what the user islooking for and their constraints), and the dialogue strategy. We propose a simple yet powerful way toimprove spoken understanding and adapt the dialogue strategy by explicitly processing the user's speechsignal through audio-processing transformer neural networks. Finally, coaching dialogues shareproperties of open-domain and task-oriented dialogues. They are somehow task-oriented but, there is norush to complete the task, and it is more important to calmly converse to make the users aware of theirown problems. In this context, we describe our collaboration in the EMPATHIC project, where a VirtualCoach capable of carrying out coaching dialogues about nutrition was built, using a modular SpokenDialogue System. Second, we model such dialogues with an end-to-end system based on TransferLearning

    Incremental Disfluency Detection for Spoken Learner English

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    Dialogue-based computer-assisted language learning (CALL) concerns the application and analysis of automated systems that engage with a language learner through dialogue. Routed in an interactionist perspective of second language acquisition, dialogue-based CALL systems assume the role of a speaking partner, providing learners the opportunity for spontaneous production of their second language. One area of interest for such systems is the implementation of corrective feedback. However, the feedback strategies employed by such systems remain fairly limited. In particular, there are currently no provisions for learners to initiate the correction of their own errors, despite this being the most frequently occurring and most preferred type of error correction in learner speech. To address this gap, this thesis proposes a framework for implementing such functionality, identifying incremental self-initiated self-repair (i.e. disfluency) detection as a key area for research. Taking an interdisciplinary approach to the exploration of this topic, this thesis outlines the steps taken to optimise an incremental disfluency detection model for use with spoken learner English. To begin, a linguistic comparative analysis of native and learner disfluency corpora explored the differences between the disfluency behaviour of native and learner speech, highlighting key features of learner speech not previously explored in disfluency detection model analysis. Following this, in order to identify a suitable baseline model for further experimentation, two state-of-the-art incremental self-repair detection models were trained and tested with a learner speech corpus. An error analysis of the models' outputs found an LSTM model using word embeddings and part-of-speech tags to be the most suitable for learner speech, thanks to its lower number of false positives triggered by learner errors in the corpus. Following this, several adaptations to the model were tested to improve performance. Namely, the inclusion of character embeddings, silence and laughter features, separating edit term detection from disfluency detection, lemmatization and the inclusion of learners' prior proficiency scores led to over an eight percent model improvement over the baseline. Findings from this thesis illustrate how the analysis of language characteristics specific to learner speech can positively inform model adaptation and provide a starting point for further investigation into the implementation of effective corrective feedback strategies in dialogue-based CALL systems

    Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018 : 10-12 December 2018, Torino

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    On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges
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