東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Spoken Language Identification from ...

Nova Southeastern University., Computer Science (CISC, CISD).

FindBook

Google Book

Amazon

博客來

Spoken Language Identification from Processing and Pattern Analysis of Spectrograms.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Spoken Language Identification from Processing and Pattern Analysis of Spectrograms./
作者:	Ford, George H., Jr.
面頁冊數:	676 p.
附註:	Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
Contained By:	Dissertation Abstracts International75-10B(E).
標題:	Computer Science. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3621947
ISBN:	9781303934490

Spoken Language Identification from Processing and Pattern Analysis of Spectrograms.
Ford, George H., Jr.

Spoken Language Identification from Processing and Pattern Analysis of Spectrograms. - 676 p.

Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.

Thesis (Ph.D.)--Nova Southeastern University, 2014.

This item must not be sold to any third party vendors.

Prior speech and linguistics research has focused on the use of phonemes recognition in speech, and their use in formulation of recognizable words, to determine language identification. Some languages have additional phoneme sounds, which can help identify a language; however, most of the phonemes are common to a wide variety of languages. Legacy approaches recognize strings of phonemes as syllables, used by dictionary queries to see if a word can be found to uniquely identify a language.

ISBN: 9781303934490Subjects--Topical Terms:

626642
Computer Science.

Spoken Language Identification from Processing and Pattern Analysis of Spectrograms.
LDR:03502nmm a2200325 4500 001 2057730
005 20150619125624.5
008 170521s2014 ||||||||||||||||| ||eng d
020 $a 9781303934490
035 $a (MiAaPQ)AAI3621947
035 $a AAI3621947
040 $a MiAaPQ $c MiAaPQ
100 1 $a Ford, George H., Jr. $3 3171611
245 1 0 $a Spoken Language Identification from Processing and Pattern Analysis of Spectrograms.
300 $a 676 p.
500 $a Source: Dissertation Abstracts International, Volume: 75-10(E), Section: B.
500 $a Adviser: Michael J. Laszlo.
502 $a Thesis (Ph.D.)--Nova Southeastern University, 2014.
506 $a This item must not be sold to any third party vendors.
520 $a Prior speech and linguistics research has focused on the use of phonemes recognition in speech, and their use in formulation of recognizable words, to determine language identification. Some languages have additional phoneme sounds, which can help identify a language; however, most of the phonemes are common to a wide variety of languages. Legacy approaches recognize strings of phonemes as syllables, used by dictionary queries to see if a word can be found to uniquely identify a language.
520 $a This dissertation research considers an alternative means of determining language identification of speech data based solely on analysis of frequency-domain data. An analytical approach to speech language identification by three comparative techniques is performed. First, a character-based pattern analysis is performed using the Rix and Forster algorithm to replicate their research on language identification. Second, techniques of phoneme recognition and their relative pattern of occurrence in speech samples are measured for performance in ability for language identification using the Rix and Forster approach. Finally, an experiment using statistical analysis of time-ensemble frequency spectrum data is assessed for its ability to establish spectral patterns for language identification, along with performance. This novel approach is applied to spectrogram audio data using pattern analysis techniques for language identification. It applies the Rix and Forster method to the ensemble of spectral frequencies used over the duration of a speech waveform. This novel approach is compared to the applications of the Rix and Forster algorithm to character-based and phoneme symbols for language identification on the basis of statistical accuracy, processing time requirements, and spatial processing resource needs. The audio spectrum analysis also demonstrates the ability to perform speaker identification using the same techniques performed for language identification.
520 $a The results of this research demonstrate the efficacy of audio frequency-domain pattern analysis applied to speech waveform data. It provides an efficient technique in language identification without reliance upon linguistic approaches using phonemes or word derivations. This work also demonstrates a quick, automated means by which information gatherers, travelers, and diplomatic officials might obtain rapid language identification supporting time-critical determination of appropriate translator resource needs.
590 $a School code: 1191.
650 4 $a Computer Science. $3 626642
650 4 $a Artificial Intelligence. $3 769149
650 4 $a Information Technology. $3 1030799
690 $a 0984
690 $a 0800
690 $a 0489
710 2 $a Nova Southeastern University. $b Computer Science (CISC, CISD). $3 1024061
773 0 $t Dissertation Abstracts International $g 75-10B(E).
790 $a 1191
791 $a Ph.D.
792 $a 2014
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3621947