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Showing 1 to 18 of 18 for “"spoken language understanding"”.
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Spoken Language Understanding in an Intelligent Tutoring Scenario
Moreover, this study tries to acknowledge the role of speech in SLU. The existing semantic analysis of speech is usually achieved through text transcription. However, human interpretation of speech and text is via different channels. We admit the role of text in semantic analysis, but we also …
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Spoken language understanding in a nutrition dialogue system
… approach to diet tracking that utilizes speech understanding and dialogue technology in order to enable efficient self-assessment of energy and nutrient consumption. We are interested in studying whether speech can lower user workload compared to existing self-assessment methods, whether spoken …
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Spoken Language Understanding: from Spoken Utterances to Semantic Structures
… two decades there have been several projects on Spoken Language Understanding (SLU). In the early nineties DARPA ATIS project aimed at providing a natural language interface to a travel information database. Following the ATIS project, DARPA Communicator project aimed at building a spoken dialog …
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Utterance verification in large vocabulary spoken language understanding system
Thesis (M.Eng.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1998.
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Incorporating non-verbal modalities in spoken language understanding for multimodal conversational systems
Thesis (Ph. D.)--Michigan State University. Computer Science, 2009
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Modeling phones, keywords, topics and intents in spoken languages
Spoken Language Understanding for both rich-resource languages (RRL) and low-resource languages (LRL) is an important research area for academia and the commercial world. In the conversational situations where either the language used in speech is a minority one, or the environment is noisy, …
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Cross-Domain and Cross-Language Porting of Shallow Parsing
… was the main focus of attention of the Natural Language Processing (NLP) community for years. As a result, there are significantly more annotated linguistic resources in English than in any other language. Consequently, data-driven tools for automatic text or speech processing are developed …
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Efficient Knowledge Transfer and Adaptation for Speech and Beyond
… a comprehensive framework for class-incremental spoken language understanding, allowing models to incrementally learn new intents and entities while retaining previously acquired knowledge. Using knowledge distillation and rehearsal-based strategies, we enhance robustness against catastrophic …
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Semantic Language models with deep neural Networks
Spoken language systems (SLS) communicate with users in natural language through speech. There are two main problems related to processing the spoken input in SLS. The first one is automatic speech recognition (ASR) which recognizes what the user says. The second one is spoken language …
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Harvesting and summarizing user-generated content for advanced speech-based human-computer interaction
… how to interpret users' intention from their spoken input correctly? Secondly, how to interpret the semantics and sentiment of user-generated data and aggregate them into structured yet concise summaries? Lastly, how to develop a dialogue modeling mechanism to handle discourse and present the …
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Characterizing and recognizing spoken corrections in human-computer dialog
Miscommunication in human-computer spoken language systems is unavoidable. Recognition failures on the part of the system necessitate frequent correction attempts by the user. Unfortunately and counterintuitively, users' attempts to speak more clearly in the face of recognition errors actually lead …
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Learning speech embeddings for speaker adaptation and speech understanding
… including automatic speech recognition (ASR) and spoken language understanding (SLU). In this dissertation, there are two main goals. The first goal is to propose modeling approaches in order to learn speaker embeddings for speaker adaptation or to learn semantic speech embeddings. The second goal …
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Discriminative methods for statistical spoken dialogue systems
… information from computer systems. Statistical spoken dialogue systems are able to disambiguate in the presence of errors by maintaining probability distributions over what they believe to be the state of a dialogue. However, traditionally these distributions have been derived using generative …
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Shallow and deep learning for audio and natural language processing
… and kernel machines for audio and natural language processing tasks are developed in this dissertation. In particular, we address the challenges for deep learning with structured relationships among data and the computational limitations of large-scale kernel machines. A general framework …
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Fact-based visual question answering using knowledge graph embeddings
… the outside world perceived through vision and language. Fact-based Visual Question Answering (FVQA), a challenging variant of VQA, requires a QA-system to mimic this human ability. It must include facts from a diverse knowledge graph (KG) in its reasoning process to produce an answer. Large …
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End-to-end Contextual Speech Recognition and Understanding
… automatic speech recognition (ASR) and spoken language understanding systems (SLU), especially for the long-tailed word problem where systems suffer from degraded performance on rare or unseen words that are both relevant to the context and carrying important information. Integrating …
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Neural Enhancement Strategies for Robust Speech Processing
… based research fields e.g. speech recognition, spoken language understanding, etc. As one of the crucial topics in the speech processing research area, speech enhancement aims to restore clean speech signals from noisy signals. In the last decades, many conventional speech enhancement …
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Deep neural network acoustic models for multi-dialect Arabic speech recognition
… signals. Arabic is one of the oldest living languages and one of the oldest Semitic languages in the world, it is also the fifth most generally used language and is the mother tongue for roughly 200 million people. Arabic speech recognition has been a fertile area of reasearch over the …