Inspired by popular co-training and domain adaptation methods, we propose a co-adaptation algorithm. The goal is improving the performance of a dialog act segmentation model by exploiting the vast amount of unlabeled data. This task provides a nice framework for multiview learning, as it has been shown that lexical and prosodic features provide complementary information. Instead of simply adding machine-labeled data to the set of manually labeled data, co-adaptation technique adapts the existing models. While both co-training and domain adaptation techniques have been employed for dialog act segmentation, our experiments show that the proposed co-adaptation algorithm results in significantly better performance. Index Terms — co-adaptation, co-training, semi-supervised learning, domain adaptation

Inspired by the recent improvements in domain adaptation and session variability compensation techniques used for speech and speaker processing, we study their effect for emotion prediction. More specifically, we investigated the use of publicly available out-of-domain data with emotion annotations for improving the performance of the in-domain model trained using 911 emergency-hotline calls. Following the emotion detection literature, we use prosodic (pitch, energy, and speaking rate) features as the inputs to a discriminative classifier. We performed segment-level n-fold cross validation emotion prediction experiments. Our results indicate significant improvement of performance for emotion prediction exploiting out-ofdomain data. Index Terms: emotion detection, domain adaptation 1.

The CALO Meeting Assistant (MA) provides for distributed meeting capture, annotation, automatic transcription and semantic analysis of multiparty meetings, and is part of the larger CALO personal assistant system. This paper presents the CALO-MA architecture and its speech recognition and understanding components, which include real-time and offline speech transcription, dialog act segmentation and tagging, topic identification and segmentation, question-answer pair identification, action item recognition, decision extraction, and summarization.

Text classification has been widely applied to many practical tasks. Inductive models trained from labeled data are the most commonly used technique. The basic assumption underlying an inductive model is that the training data are drawn from the same distribution as the test data. However, labeling such a training set is often expensive for practical applications. On the other hand, a large amount of labeled data, which have been drawn from a different distribution, is often available in the same application domain. It is thus very desirable to take advantage of these data even though there is a discrepancy between their underlying distribution and that of the test set. This paper compares three text classification algorithms applied in this scenario, including two inductive Maximum Entropy (MaxEnt) models, one flatly initialized and the other initialized with a term-frequency/inverse document frequency (Tf*Idf) weighted vector space model, and an example-based learning algorithm, which assigns a class label to a text by learning from the labels assigned to the training data that are similar to the text. Experiment results show that examplebased learning has achieved more than 5 % improvement in precisions across almost all coverage levels. Index Terms: text classification, inductive models, maximum entropy model, Tf*Idf vector space model, example-based learning. 1.

distributed meeting capture, annotation, automatic transcription and semantic analysis of multiparty meetings, and is part of the larger CALO personal assistant system. This paper presents the CALO-MA architecture and its speech recognition and understanding components, which include real-time and offline speech transcription, dialog act segmentation and tagging, topic identification and segmentation, question-answer pair identification, action item recognition, decision extraction, and summarization. Index Terms—Multiparty meetings processing, speech recognition, spoken language understanding. I.

Abstract—Logs of user queries from a search engine (such as Bing or Google) together with the links clicked provide valuable implicit feedback to improve statistical spoken language understanding (SLU) models. In this work, we propose to enrich the existing classification feature set for domain detection with features computed using the click distribution over a set of clicked URLs from search query click logs (QCLs) of user utterances. Since the form of natural language utterances differs stylistically from that of keyword search queries, to be able to match natural language utterances with related search queries, we perform a syntax-based transformation of the original utterances, after filtering out domain-independent salient phrases. This approach results in significant improvements for domain detection, especially when detecting the domains of web-related user utterances. I.

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meeting capture, annotation, automatic transcription and semantic analysis of multiparty meetings, and is part of the larger CALO personal assistant system. This paper presents the CALO-MA architecture and its speech recognition and understanding components, which include real-time and offline speech transcription, dialog act segmentation and tagging, topic identification and segmentation, question-answer pair identification, action item recognition, decision extraction, and summarization. Index Terms— multiparty meetings processing, speech recognition, spoken language understanding I.