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Voice Inspector – supporting technologies

Relevance: 8%      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)…

Save Your Time

Relevance: 8%      Posted on: 2017-06-22

If you start, the following posts might be interesting for you:   Phonexia Speech Platform is defined as an umbrella concept for all our products and services related to speech technologies. Main packages are Voice Biometrics and Speech Analytics.   Phonexia Browser PhxBrowser - application for quick tests and visualization of speech technologies results.   Speech Engine SPE3 - RESTfull API - it is adjustable server component which houses all speech technologies.   Other "good to start" pages: Academy is to help partners to understand the market, Phonexia’s products and technologies. Manuals Glossary

Licensing (technical details)

Relevance: 7%      Posted on: 2018-03-02

This document describes all licensing types for Phonexia product licensing available to our partners and customers. Each partner/customer can choose the licensing variant which best fits the current project or infrastructure. The document does not describe business conditions of Phonexia licensing. What is the License? The License is a formal agreement regarding “The Product Usage Rights” between Phonexia s.r.o. and a user of any Phonexia technology or Phonexia product. Licenses are issued by the Business Department for all speech technologies and products, and may be required in order to use utilities and tools developed by Phonexia or partners. For technical…

LID adaptation

Relevance: 7%      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…

Speech Engine configuration file explained

Relevance: 6%      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…

Components and Tools

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

This section collect information about specific components and tools of our Speech Platform.   API RESTfull API - Phonexia Speech Engine v3 (SPE3) - recommended   Apps and Tools Phonexia Browser v3 (Browser3) Voice Inspector v4 (VIN4) Voice Inspector v3 (VIN3)   You might be interested to see also Product Portfolio or End of Life Components & Tools. You might also browse our product support lifecycle policy to see which of our versions are supported and maintained.

Voice Inspector – Interpretation of results

Relevance: 6%      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…

Phonexia Academy

Relevance: 5%      Posted on: 2017-05-18

About Main idea of the Phonexia Academy is to help partners to understand the market, Phonexia’s products and technologies. Sell more, deliver your projects on time and at the highest quality, and support your clients effectively. We provide following trainings: Phonexia technologies introduction (online video course) Technical Training Essentials (online video course) Technical Training Advanced - 2 courses: Voice Biometrics Course (in-person, 2 days) Speech Analytics Course (in-person, 2 days) In Technical Training Advanced courses, we share best practices, detailed use-cases analysis, and hands on. Both courses are adjusted to our partners’ requests considering their typical projects. You might be…

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

Relevance: 5%      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…

How to convert STT confusion network results to one-best

Relevance: 5%      Posted on: 2020-04-06

Confusion Network output is the most detailed Speech Engine STT output as it provides multiple word alternatives for individual timeslots of processed speech signal. Therefore many applications want use it as the main source of speech transcription and perform eventual conversion to less verbose output formats internally. This article provides the recommended way to do the conversion. Time slots and word alternatives: The recommended algorithm for converting Confusion Network (CN) to One-best is as follows: loop through all CN timeslots from start to end in each timeslot, get the input alternative with highest score and if it's not <null/> or…