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SPE3 – Releases and Changelogs

Relevance: 100%      Posted on: 2021-04-16

Speech Engine (SPE) is developed as RESTfull API on top of Phonexia BSAPI. SPE was formerly known as BSAPI-rest (up to v2.x) or as Phonexia Server (up to v3.2.x). Releases Changelogs Speech Engine 3.40.1, DB v1700, BSAPI 3.40.1 (2021-04-16) Public release Fixed: 6th generation STT/KWS stream result may start with words from end of previous stream Fixed: Some licensing error messages are not shown in log Fixed: Missing file names in log messages in SID and SID4 tasks Fixed: Keyword list may not work if XML is used as input and optional fields threshold or pronunciations are used Fixed: phxdamin2…

Browser3 – Releases and Changelogs

Relevance: 71%      Posted on: 2021-04-06

Phonexia Browser v3 (Browser3) is developed as client on top of Phonexia Speech Engine v3. Phonexia Browser is a successor of Phonexia Speech Intelligence Resolver v1 (SIR1). This page lists changes in Browser releases. Releases Changelogs Phonexia Browser 3.40.0, BSAPI 3.40.0 (2021-03-26) Public release New: Compatibility with SPE 3.40 Changed: Using new licensing system under the hood (internal change) NOTE: When using Browser with FLS (Floating License Server), you need to upgrade FLS to version 2.x in order to be able to use Browser 3.40+ with FLS. Phonexia Browser v3.30.13, BSAPI 3.30.14 (2021-03-25) Public release Fixed: One more issue in…

LID adaptation

Relevance: 60%      Posted on: 2021-03-02

This article describes various ways of Language Identification adaptation. Basic terminology Languageprint (*.lp file) – numeric representation of the audio, extracted from audio file for language identification purpose of (similar to “voiceprint”, but representing the spoken language, not the speaking person) Languageprint archive (*.lpa file) – multiple languageprints combined into single archive Creation of languageprint archives is not supported by SPE, these are supported as input only.   Language model – digital characteristics of a specific language Language model can be trained from languageprints (*.lp), language prints archives (*.lpa), or from combination of both. LID language model should not be…

Phonexia Browser EoL

Relevance: 44%      Posted on: 2018-07-09

Information about release dates, support and maintenance periods of Phonexia Browser (software End of Life - EoL).

Voice Inspector – supporting technologies

Relevance: 44%      Posted on: 2019-06-28

Automatic Speaker Identification (SID) is the most important but not the only Phonexia technology that is implemented in Voice Inspector (VIN). Apart from SID, forensic experts, users of VIN, can benefit from automatic Signal-to-Noise Ratio calculation, Voice Activity detection, Phoneme search, and a Wave editor which incorporates the waveform, spectrum and power panel. Let's have a look on how to utilize individual technologies. Signal-to-Noise Ratio Recording quality can strongly influence the reliability of SID results and so the outcome of a forensic case. Therefore, VIN uses a module of Phonexia Speech Quality Estimation (SQE) to calculate the Signal-to-Noise Ratio (SNR)…

Phonexia Speech Engine EoL

Relevance: 34%      Posted on: 2018-06-19

Information about release dates, support and maintenance periods of Phonexia Speech Engine (software End of Life - EoL).

Voice Inspector – Interpretation of results

Relevance: 34%      Posted on: 2019-06-24

Introduction Phonexia Voice Inspector (VIN) is a tool for forensic automatic speaker identification, compliant with the Methodological Guidelines for Best Practice in Forensic Semiautomatic and Automatic Speaker Recognition, published by the European Network of Forensic Science Institutes.  This post explains individual SID score types and ways to visualize the results in a speaker identification case implemented in Voice Inspector. Evidence In VIN, the term evidence has two meanings. In general, it refers to any SID score that the system calculates for any pair of recordings in the case. These scores are the output of the Phonexia SID technology which runs…

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

Relevance: 29%      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…

SPE configuration

Relevance: 26%      Posted on: 2018-02-02

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