Prosodic Features for Sentence Segmentation Dissertation

Prosodic Features for Sentence Segmentation - thesis ExampleThe most emphasis in this approach is put on the duration of pauses between words. long-acting pauses are assumed to be sentence boundaries. The word boundary method presupposes that such pauses logically make out only at the end of sentences. This is true on many occasions since the place to pause is very at the end of sentences. The word boundary method is indeed quite useful especially when analyzing myopic sentences (Stolcke, & Shriberg, 1996, 139). The detection of sentence boundaries is one of the initial steps that get going to the understanding of speech. The fact that speech recognizer getup lacks the normal textual cues such as headers, paragraphs, sentence punctuation and capitalization was likewise mentioned. However, speech provides prosodic teaching through its durational, intonational and energy characteristics. In addition to its relevance to discourse structure in mechanical speech and its abilit y to contribute to various tasks involving the line of descent of information prosodic cues are naturally insensible(p) by word identity. It should therefore be possible to correct the robustness of lexical information extraction methods which are based on ASR (Hakkani-Tur et al 1999). Sentence segmentation is required for topic segmentation and is also needed to separate long stretches of audio data before parsing (Shriberg et al 2000). Sentence segmentation is particular for applications that are used for obtaining information from speech because information retrieval techniques such as machine translation, interrogate answering and information extraction were basically developed for text based applications (Shriberg et al 2000 Cuendet et al 2007). Kolar et al (2006, p. 629) indicates that standard automatic speech quotation systems only output a raw stream of words. It therefore means that important structural information such as punctuation is missing. Punctuation defines s entence boundaries and is wakeless to the ability of humans to understand information. Natural language treat techniques such as machine translation, information extraction and retrieval text summarization all benefit from sentence boundaries. According to Mrozinski et al (2006) self-generated speech is generally affected negatively by ungrammatical constructions and consists of false starts, word fragments and repetitions which are congressman of useless information. Output from automatic Speech-To-Text (STT) system is affected by additional problems as the word recognition error rates in spontaneous speech is still high. Sentence segmentation seat lead to an improvement in the readability and usability of such data after which automatic speech summarization can be used to extract important data. Magimai-Doss et al (2007) indicates that the aim of sentence segmentation is the enrich the improve the unstructured word sequence output for automatic speech recognition (ASR) system s with sentence boundaries in clubhouse to make further processing by humans and machines easier. Improvements in performance were shown in speech processing tasks such as speech summarization, named entity extraction and part-of-speech tagging in speech, machine translation, and for aiding human readability of the output of automatic speech recognition (ASR) systems when sentence boundary information was provided. Annotation relating to sentence boundary was found to be useful in the determination of semantically and prosodically coherent boundaries for