Performance evaluation and prediction of open source speech engine on multicore processors
2013 (English)Conference paper (Refereed) Published
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.
linear regression, multicore performance, open source, performance prediction, speech recognition, text to speech, voice driven web
IdentifiersURN: urn:nbn:se:bth-6735DOI: 10.1145/2536146.2536184Local ID: oai:bth.se:forskinfo1802B8B413D0322CC1257CBA002DEBA0ISBN: 978-145032004-7OAI: oai:DiVA.org:bth-6735DiVA: diva2:834268
5th International Conference on Management of Emergent Digital EcoSystems (MEDES)