Integration of a Large Text and Audio Corpus Using Speaker Identification

We report on an audio retrieval system which lets Internet users efficiently access a large text and audio corpus containing the transcripts and recordings of the proceedings of the United States House of Representatives. The audio has been temporally aligned to corresponding text transcripts (which are manually generated by the U.S. Government) using an automatic method based on speaker identification. This system is an example of using digital storage and structured media to make a large multimedia archive easily accessible. Introduction In the United States, the text of proceedings of the two houses of the Congress has long been published in the Congressional Record. No systematic effort has been made, however, to record audio from the floor of the House and Senate. In 1995, the non-profit Internet Multicasting Service (IMS) began sending out live streaming audio to the Internet and making complete digital audio recordings of the proceedings on computer disks. The challenge was to ..

Exploring the Internet: a technical travelogue

Includes index.Mode of access: Internet

DEC networks and architectures.

Architectuurfocu

Speaker Identification Based Text To Audio Alignment For An Audio Retrieval System

We report on an audio retrieval system which lets Internet users efficiently access a large audio database containing recordings of the proceedings of the United States House of Representatives. The audio has been temporally aligned to text transcripts of the proceedings (which are manually generated by the U.S. Government) using a novel method based on speaker identification. Speaker sequence and approximate timing information is extracted from the text transcript and used to constrain a Viterbi alignment of speaker models to the observed audio. Speakers are modeled by computing Gaussian statistics of cepstral coefficients extracted from samples of each person's speech. The speaker identification is used to locate speaker transition points in the audio which are then linked to corresponding speaker transitions in the text transcript. The alignment system has been successfully integrated into a World Wide Web based search and browse system as an experimental service on the Internet

Integration of a large text and audio corpus using speaker identification”, AAAI Spring Symposium

We report on an audio retrieval system which lets Internet users efficiently access a large text and audio corpus containing the transcripts and recordings of the proceedings of the United States House of Representatives. The audio has been temporally aligned to corresponding text transcripts (which are manually generated by the U.S. Government) using an automatic method based on speaker identification. This system is an example of using digital storage and structured media to make a large multimedia archive easily accessible

ScholarBERT: Bigger is Not Always Better

Transformer-based masked language models trained on general corpora, such as BERT and RoBERTa, have shown impressive performance on various downstream tasks. Increasingly, researchers are "finetuning" these models to improve performance on domain-specific tasks. Here, we report a broad study in which we applied 14 transformer-based models to 11 scientific tasks in order to evaluate how downstream performance is affected by changes along various dimensions (e.g., training data, model size, pretraining time, finetuning length). In this process, we created the largest and most diverse scientific language model to date, ScholarBERT, by training a 770M-parameter BERT model on an 221B token scientific literature dataset spanning many disciplines. Counterintuitively, our evaluation of the 14 BERT-based models (seven versions of ScholarBERT, five science-specific large language models from the literature, BERT-Base, and BERT-Large) reveals little difference in performance across the 11 science-focused tasks, despite major differences in model size and training data. We argue that our results establish an upper bound for the performance achievable with BERT-based architectures on tasks from the scientific domain.Comment: 16 pages. 4 figures. 8 table

Digital management of hypertension improves systolic blood pressure variability

Background: Higher systolic blood pressure variability has been shown to be a better predictor of all-cause and cardiovascular disease mortality, stroke, and cardiac disease compared with average systolic blood pressure. Methods: We evaluated the impact of a digital hypertension program on systolic blood pressure variability in 803 consecutive patients with long-standing hypertension who had been under the care of a primary care physician for a minimum of 12 months prior to enrollment (mean 4.7 years). Blood pressure readings were transmitted directly from home using a digitally connected blood pressure unit. Medication adjustments and lifestyle coaching was performed virtually via a dedicated team of pharmacists and health coaches. Systolic blood pressure variability was grouped by quartile and measured using the standard deviation (SD) of all systolic blood pressure values per individual. Results: The mean age was 67 ± 12 years, 41% were male, submitting 3.3 ± 3.7 blood pressures per week. Under usual care, only 30% of patients were in the lowest-risk quartile, and 21% of patients were in the highest risk. After 24 months, the mean systolic blood pressure variability progressively fell from 12.8 ± 4.3 mm Hg to 9.9 ± 5.1 mm Hg (