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Spoken Language Understanding (SLU) for conversational systems (SDS) aims at extracting concept and their relations from spontaneous speech. Previous approaches to SLU have modeled concept relations as stochastic semantic networks ranging from generative approach to discriminative. As spoken dialog systems complexity increases, SLU needs to perform(More)
We are interested in providing automated services via natural spoken dialog systems. By natural, we mean that the machine understands and acts upon what people actually say, in contrast to what one would like them to say. There are many issues that arise when such systems are targeted for large populations of non-expert users. In this paper, we focus on the(More)
Stochastic language models are widely used in spoken language understanding to recognize and interpret the speech signal: the speech samples are decoded into word transcriptions by means of acoustic and syntactic models and then interpreted according to a semantic model. Both for speech recognition and understanding, search algorithms use stochastic models(More)
A coherently related group of sentences may be referred to as a discourse. In this paper we address the problem of parsing coherence relations as defined in the Penn Discourse Tree Bank (PDTB). A good model for discourse structure analysis needs to account both for local dependencies at the token-level and for global dependencies and statistics. We present(More)
—We are interested in the problem of adaptive learning in the context of automatic speech recognition (ASR). In this paper, we propose an active learning algorithm for ASR. Automatic speech recognition systems are trained using human supervision to provide transcriptions of speech utterances. The goal of Active Learning is to minimize the human supervision(More)
This paper gives an overview of the research and implementation challenges we encountered in building an end-to-end natural language processing based watermarking system. With natural language watermarking, we mean embedding the watermark into a text document, using the natural language components as the carrier, in such a way that the modifications are(More)
Most research that explores the emotional state of users of spoken dialog systems does not fully utilize the contextual nature that the dialog structure provides. This paper reports results of machine learning experiments designed to automatically classify the emotional state of user turns using a corpus of 5,690 dialogs collected with the " How May I Help(More)
State-of-the-art speech recognition systems are trained using transcribed utterances, preparation of which is labor intensive and time-consuming. In this paper, we describe a new method for reducing the transcription effort for training in automatic speech recognition (ASR). Active learning aims at reducing the number of training examples to be labeled by(More)
This paper describes the AT&T WATSON real-time speech recog-nizer, the product of several decades of research at AT&T. The rec-ognizer handles a wide range of vocabulary sizes and is based on continuous-density hidden Markov models for acoustic modeling and finite state networks for language modeling. The recognition network is optimized for efficient(More)
State-of-the-art speech recognition systems are trained using human transcriptions of speech utterances. In this paper, we describe a method to combine active and unsupervised learning for automatic speech recognition (ASR). The goal is to minimize the human supervision for training acoustic and language models and to maximize the performance given the(More)