Most dialog systems explicitly confirm user-provided task-relevant concepts. User responses to these system confirmations (e.g. corrections, topic changes) may be misrecognized because they contain unrequested task-related concepts. In this paper, we propose a concept-specific language model adaptation strategy where the language model (LM) is adapted to the concept type(s) actually present in the user’s post-confirmation utterance. We evaluate concept type classification and LM adaptation for post-confirmation utterances in the Let’s Go! dialog system. We achieve 93 % accuracy on concept type classification using acoustic, lexical and dialog history features. We also show that the use of concept type classification for LM adaptation can lead to improvements in speech recognition performance. 1

In this paper, we describe CALM, a method for building statistical language models for the Web. CALM addresses several unique challenges dealing with the Web contents. First, CALM does not rely on the whole corpus to be available to build the language model. Instead, we design CALM to progressively adapt itself as Web chunks are made available by the crawler. Second, given the dynamic and dramatic changes in the Web contents, CALM is designed to quickly enrich its lexicon and N-grams as new vocabulary and phrases are discovered. To reduce the amount of heuristics and human interventions typically needed for model adaptation, we derive an information theoretical formula for CALM to facilitate the optimal adaptation in the maximum a posteriori (MAP) sense. Testing against a collection of Web chunks where new vocabulary and phrases are dominant, we show CALM can achieve comparable and satisfactory model measured in perplexity. We also show CALM is robust against over training and the initial condition, suggesting that any assumptions made in obtaining the initial model can gradually see their impacts diminished as CALM runs its full course and adapt to more data.

We investigate language model (LM) adaptation in a meeting recognition application, where the LM is adapted based on recognition output from relevant prior meetings and partial manual corrections. Unlike previous work, which has considered either completely unsupervised or supervised adaptation, we investigate a scenario where a human (e.g., a meeting participant) can correct some of the recognition mistakes. We find that recognition accuracy using the adapted LM can be enhanced substantially by partial correction. In particular, if all content words (about half of all recognition errors) are corrected, recognition improves to the same accuracy as if completely error-free (manually created) transcriptions had been used for adaptation. We also compare and combine a variety of adaptation methods, including linear interpolation, unigram marginal adaptation, and a discriminative method based on “positive ” and “negative” N-grams. Index Terms — speech processing, language modeling, meeting recognition, unsupervised adaptation, user feedback.

Dialog act (DA) segmentation in meeting speech is important for meeting understanding. In this paper, we explore speaker adaptation of hidden event language models (LMs) for DA segmentation using the ICSI Meeting Corpus. Speaker adaptation is performed using a linear combination of the generic speakerindependent LM and an LM trained on only the data from individual speakers. We test the method on 20 frequent speakers, on both reference word transcripts and the output of automatic speech recognition. Results indicate improvements for 17 speakers on reference transcripts, and for 15 speakers on automatic transcripts. Overall, the speaker-adapted LM yields statistically significant improvement over the baseline LM for both test conditions.

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.

The CALO Meeting Assistant 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 summarizes the CALO-MA architecture and its speech recognition and understanding components, which include real-time and offline speech transcription, dialog act segmentation and tagging, question-answer pair identification, action item recognition, decision extraction, and summarization. Index Terms — multiparty meetings processing, speech recognition, spoken language understanding 1.

In limited data domains, many effective language modeling techniques construct models with parameters to be estimated on an in-domain development set. However, in some domains, no such data exist beyond the unlabeled test corpus. In this work, we explore the iterative use of the recognition hypotheses for unsupervised parameter estimation. We also evaluate the effectiveness of supervised adaptation using varying amounts of user-provided transcripts of utterances selected via multiple strategies. While unsupervised adaptation obtains 80 % of the potential error reductions, it is outperformed by using only 300 words of user transcription. By transcribing the lowest confidence utterances first, we further obtain an effective word error rate reduction of 0.6%. Index Terms — speech recognition, language modeling, adaptation

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.

The CALO Meeting Assistant 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 summarizes the CALO-MA architecture and its speech recognition and understanding components, which include real-time and offline speech transcription, dialog act segmentation and tagging, question-answer pair identification, action item recognition, decision extraction, and summarization. Index Terms — multiparty meetings processing, speech recognition, spoken language understanding 1.

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.