constraint based speaker independent yes/no automatic speech recognizer.
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constraint based speaker independent yes/no automatic speech recognizer. by Daniel McCartney

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Published by The author] in [S.l .
Written in English

Book details:

Edition Notes

Thesis (M. Sc. (Computing and Design)) - University of Ulster, 1995.

The Physical Object
Paginationiv,169p. :
Number of Pages169
ID Numbers
Open LibraryOL19097622M

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  The most general form of speaker recognition (text-independent) is still not very accurate for large speaker populations, but if the words spoken by the user are constrained and the speech quality is not allowed to vary too wildly, then it too can be done on a workstation. Lee, Automatic Speech Recognition: the development of the Sphinx Cited by: A method for speaker independent connected word recognition is described. Speaker independence is achieved by clustering isolated word utterances of a speaker population. Connected word recognition is based on a syntax-directed dynamic programming algorithm which matches the isolated word templates to sentence length by: 1. Basically, MAP adaptation needs a data storage for speaker adaptive (SA) model as much as speaker independent (SI) model needs. Modern speech recognition systems . Automatic speech recognition (ASR) is an independent, machine-based process of decoding and transcribing oral speech. A typical ASR system receives acoustic input from a speaker through a.

  Recently, it has been demonstrated that speech recognition systems are able to achieve human parity. While much research is done for resource-rich languages like English, there exists a .   Over the past years several users (in Belgium, the Netherlands and abroad) have adopted the ESAT speech recognition software package (developed for over 15 years at ESAT, , [5, 10]) as they found that it satisfied their research needs better than other available r, typical of organically grown software, the learning effort was considerable and . In speaker recognition there are only information depending on an act. The state of-the-art approach to automatic speaker verification (denoted as ASV) is to build a stochastic model of a speaker, based on speaker characteristics extracted from the available amount of training speech. In speaker recognition we differ between low-level and high. Speech recognition engines that are speaker independent generally deal with this fact by limiting the grammars they use. By using a smaller list of recognized words, the speech engine is more likely to correctly recognize what a speaker said. This makes speaker–independent software ideal for most IVR systems, and any application where a large.

technology. speech recognition speaker recognition, focusing on current technology applied to personal worksta- of speech processing Factors affecting tions. Limited forms of speech recognition are available on personal workstations. Cur- rently there is much interest in speech recognition, and performance is improv- ing. Unlike text-independent speaker verification system [3][4][5][6], which is a process of verifying the identity without constraint on the speech content, text-dependent speaker verification. utilizing only an automatic speech recognition system trained on (native) L2 speech. A text-independent approach using fea-tures based on voiced segments works even completely without speech recognition. The paper is organized as follows. Section 2 presents the collection and annotation of the speech data used for training and evaluation. Using speaker-independent models, the authors studied speaker-adaptive recognition. Both codebooks and output distributions were considered for adaptation. It was found that speaker-adaptive systems outperform both speaker-independent and speaker-dependent systems, suggesting that the most effective system is one that begins with speaker.