Change search
ReferencesLink to record
Permanent link

Direct link
Performance evaluation and prediction of open source speech engine on multicore processors
Blekinge Institute of Technology, School of Computing.
Blekinge Institute of Technology, School of Computing.
2013 (English)Conference paper (Refereed) Published
Abstract [en]

This paper quantifies the performance of the core part of voice driven web using free and open source speech engine; the speech engine which is very high computation demanding, it consists of Automatic Speech Recognition (ASR) and Text To Speech (TTS). Two open source programs, Sphinx-4 and FreeTTS-1.2.2 are used for ASR and TTS respectively. These two programs are executed on 2 different hardware multicore processors with 4 hyperthreaded cores, and 8 cores respectively. The response time with respect to the load variance and the number of cores is measured and predicted using a linear regression model. The results show that, the response time is linear with respect to the input length, this property can be used to directly predict the response for any input length. Moreover, though the response time and the speed up increases as the number of cores increases, the regression coefficients and number of threads reveal that ASR benefits from multicore. The speedup factor for ASR is 1.56 for 8 cores. However for FreeTTS, though being sequential the speed up from the program itself is insignificant, there is about 1. 43 speedup for 8 cores, that comes from the system's contribution. Our findings show that the generalization of the results for multicore processor does not apply to hyperthreading. This paper presents the investigation that is useful for educators, researchers, and applications' developer in voice based applications 'domain.

Place, publisher, year, edition, pages
Luxembourg: ACM , 2013.
Keyword [en]
linear regression, multicore performance, open source, performance prediction, speech recognition, text to speech, voice driven web
National Category
Software Engineering
URN: urn:nbn:se:bth-6735DOI: 10.1145/2536146.2536184Local ID: 978-145032004-7OAI: diva2:834268
5th International Conference on Management of Emergent Digital EcoSystems (MEDES)
Available from: 2014-04-14 Created: 2014-04-14 Last updated: 2015-06-30Bibliographically approved

Open Access in DiVA

fulltext(636 kB)34 downloads
File information
File name FULLTEXT01.pdfFile size 636 kBChecksum SHA-512
Type fulltextMimetype application/pdf

Other links

Publisher's full text

Search in DiVA

By author/editor
Niyizamwiyitira, ChristineLundberg, Lars
By organisation
School of Computing
Software Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 34 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

Altmetric score

Total: 16 hits
ReferencesLink to record
Permanent link

Direct link