The Infona portal uses cookies, i.e. strings of text saved by a browser on the user's device. The portal can access those files and use them to remember the user's data, such as their chosen settings (screen view, interface language, etc.), or their login data. By using the Infona portal the user accepts automatic saving and using this information for portal operation purposes. More information on the subject can be found in the Privacy Policy and Terms of Service. By closing this window the user confirms that they have read the information on cookie usage, and they accept the privacy policy and the way cookies are used by the portal. You can change the cookie settings in your browser.
Automatic sentence segmentation of speech is important for enriching speech recognition output and aiding downstream language processing. This paper focuses on automatic sentence segmentation of speech in two different languages - English and Czech. For this task, we compare and combine three statistical models - HMM, maximum entropy, and a boosting-based model BoosTexter. All these approaches rely...
This paper describes a normalization system for text messages to allow them to be read by a TTS engine. To address the large number of texting abbreviations, we use a statistical classifier to learn when to delete a character. The features we use are based on character context, function, and position in the word and containing syllable. To ensure that our system is robust to different abbreviations...
We investigate genre effects on the task of automatic sentence segmentation, focusing on two important domains - broadcast news (BN) and broadcast conversation (BC). We employ an HMM model based on textual and prosodic information and analyze differences in segmentation accuracy and feature usage between the two genres using both manual and automatic speech transcripts. Experiments are evaluated using...
In this paper, we present a hidden Markov model (HMM) approach to segment meeting transcripts into topics. To learn the model, we use unsupervised learning to cluster the text segments obtained from topic boundary information. Using modified WinDiff and Pk metrics, we demonstrate that an HMM outperforms LCSeg, a state-of-the-art lexical chain based method for topic segmentation using the ICSI meeting...
Set the date range to filter the displayed results. You can set a starting date, ending date or both. You can enter the dates manually or choose them from the calendar.