N-best speech hypotheses reordering using linear regression

We propose a hypothesis reordering technique to improve speech recognition accuracy in a dialog system. For such systems, additional information external to the decoding process itself is available, in particular features derived from the parse and the dialog. Such features can be combined with recognizer features by means of a linear regression model to predict the most likely entry in the hypothesis list. We introduce the use of concept error rate as an alternative accuracy measurement and compare it withy the use of word error rate. The proposed model performs better than human subjects performing the same hypothesis reordering task. 1

N-best Speech Hypotheses Reordering Using Linear Regression

We propose a hypothesis reordering technique to improve speech recognition accuracy in a dialog system. For such systems, additional information external to the decoding process itself is available, in particular features derived from the parse and the dialog. Such features can be combined with recognizer features by means of a linear regression model to predict the most likely entry in the hypothesis list. We introduce the use of concept error rate as an alternative accuracy measurement and compare it withy the use of word error rate. The proposed model performs better than human subjects performing the same hypothesis reordering task

N-best Speech Hypotheses Reordering Using Linear Regression

We propose a hypothesis reordering technique to improve speech recognition accuracy in a dialog system. For such systems, additional information external to the decoding process itself is available, in particular features derived fromthe parse and the dialog. Such features can be combined with recognizer features by means of a linear regression model to predict the most likely entry in the hypothesis list. We introduce the use of concept error rate as an alternative accuracy measurement and compare it withy the use of word error rate. The proposed model performs better than human subjects performing the same hypothesis reordering task.</p

Automatic Concept Identification In Goal-Oriented Conversations

We address the problem of identifying key domain conceptsautomatically from an unannotated corpus of goal-orientedhuman-human conversations. We examine two clusteringalgorithms, one based on mutual information and another onebased on Kullback-Liebler distance. In order to compare theresults from both techniques quantitatively, we evaluate theoutcome clusters against reference concept labels usingprecision and recall metrics adopted from the evaluation oftopic identification task. However, since our system allowsmore than one cluster to associate with each concept anadditional metric, a singularity score, is added to better capturecluster quality. Based on the proposed quality metrics, theresults show that Kullback-Liebler-based clusteringoutperforms mutual information-based clustering for both theoptimal quality and the quality achieved using an automaticstopping criterion</p

Acquiring Domain-Specific Dialog Information from Task-Oriented Human-Human Interaction through an Unsupervised Learning

We  describe  an approach for acquiring the domain-specific dialog knowledge required to configure  a  task-oriented  dialog system  that uses human-human interaction data. The key aspects of this problem are the design of a dialog information representation and a learning approach  that supports capture of  domain information from in-domain  dialogs. To represent a dialog for a learning purpose,  we based our representation, the  form-based dialog structure representation, on an observable structure. We show that this representation is sufficient for modeling phenomena that occur regularly in  several dissimilar  taskoriented  domains, including informationaccess and  problem-solving. With the goal of ultimately  reducing human  annotation  effort, we examine the use of unsupervised learning techniques in acquiring the components of the form-based representation (i.e. task,  subtask, and concept). These techniques include statistical word clustering based on mutual information and  Kullback-Liebler distance, TextTiling, HMM-based segmentation, and bisecting  K-mean document clustering.  Withsome modifications to make these algorithms more suitable for inferring  the structure of a spoken dialog, the unsupervised learning algorithms show promise.</p

Combining Trigram and Winnow in Thai OCR Error Correction

For languages that have no explicit word boundary such as Thai, Chinese and Japanese, correcting words in text is harder than in English because of additional ambiguities in locating er- ror words. The traditional method handles this by hypothesizing that every substrings in the input sentence could be error words and trying to correct all of them. In this paper, we propose the idea of reducing the scope of spelling correction by focusing only on dubious areas in the input sentence. Boundaries of these dubious areas could be obtained approximately by applying word segmentation algorithm and finding word sequences with low probability. To generate the candidate correction words, we used a modified edit distance which reflects the charac- teristic of Thai OCR errors. Finally, a part-ofspeech trigram model and Winnow algorithm are combined to determine the most probable correction