Integrating Prosodic and Lexical Cues for Automatic Topic Segmentation

We present a probabilistic model that uses both prosodic and lexical cues for the automatic segmentation of speech into topically coherent units. We propose two methods for combining lexical and prosodic information using hidden Markov models and decision trees. Lexical information is obtained from a speech recognizer, and prosodic features are extracted automatically from speech waveforms. We evaluate our approach on the Broadcast News corpus, using the DARPA-TDT evaluation metrics. Results show that the prosodic model alone is competitive with word-based segmentation methods. Furthermore, we achieve a significant reduction in error by combining the prosodic and word-based knowledge sources.Comment: 27 pages, 8 figure

Prosody-Based Automatic Segmentation of Speech into Sentences and Topics

A crucial step in processing speech audio data for information extraction, topic detection, or browsing/playback is to segment the input into sentence and topic units. Speech segmentation is challenging, since the cues typically present for segmenting text (headers, paragraphs, punctuation) are absent in spoken language. We investigate the use of prosody (information gleaned from the timing and melody of speech) for these tasks. Using decision tree and hidden Markov modeling techniques, we combine prosodic cues with word-based approaches, and evaluate performance on two speech corpora, Broadcast News and Switchboard. Results show that the prosodic model alone performs on par with, or better than, word-based statistical language models -- for both true and automatically recognized words in news speech. The prosodic model achieves comparable performance with significantly less training data, and requires no hand-labeling of prosodic events. Across tasks and corpora, we obtain a significant improvement over word-only models using a probabilistic combination of prosodic and lexical information. Inspection reveals that the prosodic models capture language-independent boundary indicators described in the literature. Finally, cue usage is task and corpus dependent. For example, pause and pitch features are highly informative for segmenting news speech, whereas pause, duration and word-based cues dominate for natural conversation.Comment: 30 pages, 9 figures. To appear in Speech Communication 32(1-2), Special Issue on Accessing Information in Spoken Audio, September 200

Integrating lexical and prosodic features for automatic paragraph segmentation

Spoken documents, such as podcasts or lectures, are a growing presence in everyday life. Being able to automatically identify their discourse structure is an important step to understanding what a spoken document is about. Moreover, finer-grained units, such as paragraphs, are highly desirable for presenting and analyzing spoken content. However, little work has been done on discourse based speech segmentation below the level of broad topics. In order to examine how discourse transitions are cued in speech, we investigate automatic paragraph segmentation of TED talks using lexical and prosodic features. Experiments using Support Vector Machines, AdaBoost, and Neural Networks show that models using supra-sentential prosodic features and induced cue words perform better than those based on the type of lexical cohesion measures often used in broad topic segmentation. Moreover, combining a wide range of individually weak lexical and prosodic predictors improves performance, and modelling contextual information using recurrent neural networks outperforms other approaches by a large margin. Our best results come from using late fusion methods that integrate representations generated by separate lexical and prosodic models while allowing interactions between these features streams rather than treating them as independent information sources. Application to ASR outputs shows that adding prosodic features, particularly using late fusion, can significantly ameliorate decreases in performance due to transcription errors.The second author was funded from the EU’s Horizon 2020 Research and Innovation Programme under the GA H2020-RIA-645012 and the Spanish Ministry of Economy and Competitivity Juan de la Cierva program. The other authors were funded by the University of Edinburgh

Teenage and Adult Speech in School Context: Building and Processing a Corpus of European Portuguese

We present a corpus of European Portuguese spoken by teenagers and adults in school context, CPE-FACES, with an overview of the differential characteristics of high school oral presentations and the challenges this data poses to automatic speech processing. The CPE-FACES corpus has been created with two main goals: to provide a resource for the study of prosodic patterns in both spontaneous and prepared unscripted speech, and to capture inter-speaker and speaking style variations common at school, for research on oral presentations. Research on speaking styles is still largely based on adult speech. References to teenagers are sparse and cross-analyses of speech types comparing teenagers and adults are rare. We expect CPE-FACES, currently a unique resource in this domain, will contribute to filling this gap in European Portuguese. Focusing on disfluencies and phrase-final phonetic-phonological processes we show the impact of teenage speech on the automatic segmentation of oral presentations. Analyzing fluent final intonation contours in declarative utterances, we also show that communicative situation specificities, speaker status and cross gender differences are key factors in speaking style variation at school.info:eu-repo/semantics/publishedVersio

Children at risk : their phonemic awareness development in holistic instruction

Includes bibliographical references (p. 17-19