Search Results for: performance measurement

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Performance of the Speaker Identification 4th generation (SID4): Intel® Xeon® Platinum 8124M

Relevance: 100%      Posted on: 2019-10-30

Benchmark goals Find realistic performance using total recording length Find FTRT based exactly on net_speech (engineering sizing data) Find system performance using all physical cores Find system performance using all logical cores Infrastructure setup Intel® Xeon® Platinum 8124M is used in virtual machine with 8 physical cores reserved exclusively for this VM, Hyper Threading is enabled [16 logical cores available], 32GB RAM, 30GB SSD based storage, 1000 I/O.s-1  reserved per core Benchmark data setup Data set statistic: Number of files: 32 [300 seconds each] RAW recordings length ∑: 9600 [sec] Net speech length ∑: 4224.77 [sec] In the data set…

Measuring of a software processing speed – what is the FtRT (Faster than Real Time)

Relevance: 16%      Posted on: 2019-10-30

Faster Than Real Time (FTRT) is metrics developed for defining software performance reference point. Using this metric you can collect "benchmark" data of real processing speed for reviewed software, which should be found - and reproduced - on exactly defined HW. Then, comparing various benchmarks result, you can compare performance of the specified software and its parts on different HW configurations. And vice versa - using the same metric you can compare software from different vendors on the same HW configuration and for the same processing task. We are recognizing two measurable metrics: Recording based FTRT is calculated from real…

Sizing of the computing units for speech technologies

Relevance: 9%      Posted on: 2018-02-02

Best practices for good sizing of Phonexia technologies depend on a few facts: Intense work with large data sets requires good performance and bandwidth between RAM and CPU. It all depends on the size of the files with technological models data, usually loaded into RAM and used intensively for computing operations Always think only about physical cores of CPU (HT, VT features can't help in performance) Also seek for CPUs with a large L3 cache. And the better CPUs are those with higher l3_cache_size/#_of_physical_CPU_cores ratio. We currently assume that CPUs from the current Intel Xeon Family in the 4th generation…

Speech Engine configuration file explained

Relevance: 9%      Posted on: 2021-02-19

In this article we explain details of the Speech Engine configuration file phxspe.properties, located in settings subdirectory in SPE installation location. Settings in this configuration file affect the Speech Engine behavior and performance. The configuration file is usually created after SPE installation – on first use of phxadmin, a default configuration filephxspe.properties is created in the settings directory. The file is loaded during SPE startup, i.e. you need to restart SPE to apply any changes made in the file. If Speech Engine is used together with Phonexia Browser in so-called "embedded" mode (see details about "embedded SPE" mode in Browser…

Voice Biometrics Course (technical training)

Relevance: 6%      Posted on: 2017-05-18

The Voice Biometrics course consist of the following modules. Please ask your Phonexia contact for detailed description. (YES = this part is mandatory for course)   VBS course Required time [h] Block name Block description YES 0,5 Intro & Phonexia Portfolio Intro & Phonexia Portfolio YES 0,5 Project focus - Explain basic needs Partner project related discussion focused mainly to finalizing training topics and agenda YES 0,75 Apps Designing and Developing - Licensing Gives trainee knowledge about type of licensing, and how to use the license file YES 0,75 Technologies - Data gathering and Quality measurement - basic Data gathering…

Speech Analytics Course (technical training)

Relevance: 6%      Posted on: 2017-05-18

The Speech Analytics course consists of the following modules. Please ask your Phonexia contact for detailed description. (YES = this part of the course is obligatory)   SAL course Required time [h] Block name Block description YES 0,5 Intro & Phonexia Portfolio Intro & Phonexia Portfolio YES 0,5 Project focus – Explain basic needs Discussion of partner project focused mainly on finalizing the training topics and agenda. YES 0,75 Application Design & Development – Licensing Presentation of types of licensing, and how to use the license file. YES 0,75 Technologies – Data gathering and Quality measurement – basic Description of…

SPE configuration

Relevance: 5%      Posted on: 2018-02-02

Basic explanation of configuration directives for SPE with hints & tips. Overview of phxspe.properties for beginners.

Speaker Identification (SID)

Relevance: 5%      Posted on: 2019-06-13

Phonexia Speaker Identification uses the power of voice biometry to recognize speakers by their voice... i.e. to decide whether the voice in two recordings belongs to the same person or two different people. High accuracy of Speaker Identification, the Phonexia's flagship technology, has been validated in a NIST Speaker Recognition Evaluations. Basic use cases and application areas The technology can be used for various speaker recognition tasks. One basic distinction is based on the kind of question we want to answer. Speaker Identification is the case when we are asking "Whose voice is this?", such as in fake emergency calls.…

Terminology

Relevance: 2%      Posted on: 2017-06-15

Document which briefly describes processes and relations in Phonexia Technologies with consideration on correct word usage.   SID - Speaker Identification Technology (about SID technology) which recognize the speaker in the audio based on the input data (usually database of voiceprints). XL3, L3,L2,S2 - Technology models of SID. Speaker enrollment - Process, where the speaker model is created (usually new record in the voiceprint database). Speaker model: 1/ should reach recommended minimums (net speech, audio quality), 2/ should be made with more net speech and thus be more robust. The test recordings (payload) are then compared to the model (see…

WER

Relevance: 2%      Posted on: 2018-02-01

Word Error Rate – metrics for STT/LVCSR accuracy measurement