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SPE configuration

Relevance: 100%      Posted on: 2018-02-02

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

How to configure Speech Engine workers

Relevance: 82%      Posted on: 2020-03-28

Worker is a working thread performing the actual files- or realtime streams processing in Speech Engine. This article helps to understand the Speech Engine workers and provides information how to configure workers for optimal performance and server utilization. The default workers configuration in settings/phxspe.properties is as shown below – 8 workers for files processing and 8 workers for realtime streams processing. These numbers mean the maximum number of simultaneously running tasks. # Multithread settings server.n_workers = 8 server.n_realtime_workers = 8 Requests for additional file processing tasks are put in a queue and processed according their order and priorities. Requests for…

SPE3 – Releases and Changelogs

Relevance: 77%      Posted on: 2020-09-12

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). This page lists changes in SPE releases. Releases Changelogs Speech Engine 3.30.13 (09/11/2020) - DB v1401, BSAPI 3.30.13 Public release New: Updated STT and KWS model AR_XL to version 5.1.0 Speech Engine 3.32.0 (08/28/2020) - DB v1500, BSAPI 3.32.0 Non-public Feature Preview release New: Added support for Webhooks and WebSockets in stream processing New: Added support for preferred phrases in 5th generation of STT (see POST /technologies/stt or POST /technologies/stt/input_stream) New:…

Terminology

Relevance: 55%      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…

Time Analysis (TAE)

Relevance: 27%      Posted on: 2017-05-18

Technology description Technology Time Analysis Extraction by Phonexia extracts base information from dialogue in a recording, providing essential knowledge about conversation flow. That makes it easy to identify long reaction time, crosstalk, or responses of speakers in both channels.  This technology is only meaningful when used on recordings with 2 channels. As an answer to the TAE technology, SPE returns a json/xml file. This file includes general information about the technology and details of the time analysis. The technology can work either with a closed recording or with a stream. Monologue Describes the statistics of a recording related to one…

Time Analysis

Relevance: 27%      Posted on: 2018-04-15

Time Analysis Extraction (TAE) by Phonexia extracts base information from dialogue in a recording, providing essential knowledge about conversation flow. That makes it easy to identify long reaction time, crosstalk, or responses of speakers in both channels. This technology is only meaningful when used on recordings with 2 channels. As an answer to the TAE technology, SPE returns a json/xml file. This file includes general information about the technology and details of the time analysis. The technology can work either with a closed recording or with a stream. Monologue Describes the statistics of a recording related to one channel. channel…

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

Relevance: 27%      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…

Licensing (technical details)

Relevance: 22%      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…

Language Identification results explained

Relevance: 22%      Posted on: 2019-05-20

This article aims on giving more details about Language Identification scoring and hints on how to tailor Language Identification to suit best your needs. Scoring and results explanation When Phonexia Language Identification identifies a language in audio recording (or languageprint) using a language pack, it creates languageprint of the recording (if input is audio recording) compares that languageprint with each language in a language pack and calculates probability that these two languages are the same The final scores are returned as logarithms of these individual probabilities – i.e. as values from {-inf,0} interval – for each language in the language pack.…

Terms of Service

Relevance: 20%      Posted on: 2018-03-24

Description of the Services provided by Phonexia s.r.o. 1. Acceptance of Terms of Service (Terms as a Contract) 1.1. PHONEXIA-User Relationship. These Terms of Service (hereinafter referred to as "Agreement" or „Terms of Service“) and the PHONEXIA Privacy Policy govern the relationship between Phonexia s.r.o. (ID No.: 27680258, VAT No.: CZ27680258, registred seat at: Chaloupkova 3002/1a, 61200 Brno, registred by the County Court in Brno under file C, insert 5124), provider of the PHONEXIA technology (hereinafter referred to as "PHONEXIA") and you ("you", "your", „user“ or "Member"), and your use of and access to the website, PHONEXIA services or any…

Speaker Identification (SID)

Relevance: 18%      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.…

Keyword

Relevance: 18%      Posted on: 2018-04-04

Word or a phrase that is searched by a user (defined by a user as an input for KWS technology). Phonexia does not limit the number of keywords in the keyword list. The higher number of keywords (500+) cause speed decrease.

Packages, Updates vs. Upgrades

Relevance: 16%      Posted on: 2018-04-15

Our packages follow the bug-fix /updates / upgrades approach. Some packages are distributed with limited set of speech technologies or without speech technologies. Packages Our software is distributed as ZIP file. Installation procedure is matter of unzipping archive, reconfiguration and start of software. SPE and VIN package contains speech technologies (note: SPE might contain only selected technologies).  PhxBrowser does not contain speech technologies and it needs to be combined with SPE. The software is activated by licensing file. Updates vs. Upgrades Bugfix By bugfix we understand a fix of known problem without changing components or technology models. Bugfix changes only…

Speaker Identification: Results Enhancement

Relevance: 16%      Posted on: 2019-05-29

Speaker Identification (SID) Results Enhancement is a process that adjusts the score threshold for detecting/rejecting speakers by removing the effect of speech length and audio quality. This is achieved by use of Audio Source Profiles, that represent as closely as possible the source of the speech recording (device, acoustic channel, distance from microphone, language, gender, etc.). Although the out-of-the-box system is robust in such factors, several result enhancement procedures can provide even better results and stronger evidence. Audio Source Profile An Audio Source Profile is a representation of the speech source, e.g., device, acoustic channel, distance from microphone, language, gender,…

Voice Inspector – supporting technologies

Relevance: 15%      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)…

Sizing of the computing units for speech technologies

Relevance: 13%      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…

Keyword Spotting

Relevance: 13%      Posted on: 2019-06-03

Phonexia Keyword Spotting (KWS) identifies occurrences of keywords and/or keyphrases in audio recordings. It can help you to get valuable information from huge quantities of speech recordings. You only need to specify the keywords or phrases you wish to find. This technology identifies all recordings with keyword occurrences and allows you to automatically route important recordings or calls to your experts. Typical use cases Call centers increase operator and supervisor efficiency by searching calls identify inappropriate expressions from operators check marketing campaigns with automatic script-compliance control Mass media and web search servers index and search multimedia by keyword route multimedia…

Keyword list

Relevance: 13%      Posted on: 2018-04-04

Set of keywords or phrases defined together with keyword pronunciations (Phonexia do not limit the number of keyword lists).

Keyword pronunciation

Relevance: 13%      Posted on: 2018-04-04

Pronunciation of the keyword(s) is generated automatically (G2P, grapheme to phoneme)  or produced from the lexicon of known words (“lexicon”) or converted from audio (phoneme transcription). It can be edited manually for each word (Phonexia do not limit the number of pronunciations per keywords/phrases).

Voice Inspector

Relevance: 11%      Posted on: 2017-05-18

About Phonexia Voice Inspector (VIN) provides police forces and forensic experts with a highly accurate speaker identification tool during investigation of criminal matters. It uses the power of voice biometry to automatically recognize speakers by their voice. Main features of the VIN application: Automatic speaker identification tool to strengthen results of the standard phonetics-based approaches Scoring in likelihood ratio (LR) – Result from statistical test for two models comparison. It gives back number which expresses how many times more likely the data are under one model than the other. LnLR or LogLR meets numbers in interval <-∞;+∞>...), and verbal presentation…

Speech Intelligence Resolver v1

Relevance: 11%      Posted on: 2017-05-18

About Phonexia Speech Intelligence Resolver v1 (SIR1) combines the power of speech technologies within a single application. The application automatically performs visualization of the record as well as filtering the speech metadata uncovered from your records effectively. Speech technologies implemented: Phonexia Speaker Identification (SID2) Phonexia Language Identification (LID2) Phonexia Gender identification (GID) Phonexia Voice Activity Detection (VAD) Phonexia Speaker Diarization (DIAR) Phonexia Keyword Spotting (KWS) Phonexia Speech Quality Estimator (SQE) Phonexia Speech Transcription (STT) SIR is a client application cooperating with REST servers. It can be used as a standalone application due to the integrated local REST server. It was…

Q: What LLR, LR and score mean?

Relevance: 11%      Posted on: 2017-06-27

A: These abbreviations mean the following: LR - likelihood ratio, result from statistical test for two models comparison. It returns a number which expresses how many times more likely the data are under one model than the other.  LR meets numbers in interval <0;+inf). LLR - abbreviation for log-likelihood ratio statistic, logarithmic function of LR. LLR meets numbers in interval (-inf;+inf). Percentage (normalised) score - commonly used mathematical transformation of the LLR to percentage. This number is better for human readability but may bring some doubts if LLR numbers are too high (typically for some non-adapted installations). Interval <0;100> (or…

Voice Biometrics

Relevance: 11%      Posted on: 2018-04-07

Overview Phonexia Voice Biometrics is a special edition of Phonexia Speech Platform which allows you to understand the nature of audio without having to listen to it. The product helps people to utilize the power of voice biometrics to verify speaker or identify crimes. The technologies reveals automatically WHO, what GENDER, what LANGUAGE is speaking, and many other metadata. Voice Biometrics - Typical Use-Cases Use case Speaker Verification is tailored to banks/insurance companies/money lending companies and others, where is needed to confirm if caller/voice in audio file is the same person who is known to the customer. For this use…

Voice Activity Detection – Essential

Relevance: 11%      Posted on: 2018-04-04

Phonexia Voice Activity Detection (VAD) identifies parts of audio recordings with speech content vs. nonspeech content. Technology Trained with emphasis on spontaneous telephony conversation The technology is language-, accent-, text-, and channel- independent Compatibility with the widest range of audio sources possible (applies channel compensation techniques): GSM/CDMA, 3G, VoIP, landlines, etc. Input Input format for processing: WAV or RAW (8 or 16 bits linear coding), A-law or Mu-law, PCM, 8kHz+ sampling Output Log file with processed information (speech vs. nonspeech segments) Segmentation The section Segmentation describes the results of VAD, which are segments of detected voice and silence. Segments are…

Phonexia End User License Agreement

Relevance: 11%      Posted on: 2019-02-27

Please read the terms and conditions of this End User License Agreement (the “Agreement”) carefully before you use the Phonexia proprietary software providing speech solutions, technologies and accompanying services (the “Software”) delivered and marketed by Phonexia s.r.o.

Browser3 – Releases and Changelogs

Relevance: 11%      Posted on: 2020-08-21

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 v3.30.12, BSAPI 3.30.11 - Aug 20 2020 Public release Fixed: Transcription results intermittently displays words in wrong order Versions 3.30.9, 3.30.10 and 3.30.11 were skipped Phonexia Browser v3.31.3, BSAPI 3.30.11 - Aug 20 2020 Non-public Feature Preview release Fixed: Transcription results intermittently displays words in wrong order Phonexia Browser v3.31.2, BSAPI 3.31.0 - Jul 24 2020 Non-public Feature Preview release Fixed: STT…

Phonexia Speech Engine

Relevance: 11%      Posted on: 2017-05-18

About Phonexia Speech Engine v3 (SPE3) is a main executive part of the Phonexia Speech Platform. It is a server application with REST API interface through which you can access all available speech technologies. Both, Linux 64bit and Windows 64bit operating systems are supported. Phonexia Speech Engine (SPE3) is adjustable server component which houses all speech technologies. SPE3 provides RESTfull application programming interface to access various technologies. Aside from technologies themselves the SPE has implemented other various functionality supporting work with speech technologies, recordings and streams, and others. Features Main purpose of SPE is to work as processing unit for…

Keyword Spotting (KWS)

Relevance: 11%      Posted on: 2017-05-18

About KWS Phonexia Keyword Spotting (KWS) identifies occurrences of key-words and/or key-phrases in audio recordings. Application areas: Security/defense Maintain fast reaction times by routing calls with specific content to human operators Search for specific information in large call archives Trigger alarms immediately (online) when an event occurs Call centers Increase operator and supervisor efficiency by searching calls Identify inappropriate expressions from operators Check marketing campaigns with automatic script compliance control Mass media and web search servers Index and search multimedia by keyword Route multimedia files and streams according to their content   KWS technology Acoustic based technology robust even with…

Prefiltering

Relevance: 5%      Posted on: 2018-03-23

Prefiltering is a very important part of basically any speech technology architecture. These 2 technologies are very fast and can significantly decrease the load and increase the precision of the following technologies (the exact number depends on the type of your data), thanks to sorting out the files with unacceptable quality or not enough net speech. The 2 technologies in question are Speech Quality Estimation (SQE) and Voice Activity Detection (VAD).  

Phonexia Voice Inspector v3

Relevance: 5%      Posted on: 2018-04-02

About Phonexia Voice Inspector v3 (VIN3) provides police forces and forensic experts with a highly accurate speaker identification tool during investigation of criminal matters. It uses the power of voice biometry to automatically recognize speakers by their voice. Main features of the VIN3 application: Automatic speaker identification tool to strengthen results of the standard linguistics- and phonetics-based approach Scoring in Likelihood Ratio (LR) – result from a statistical test for a comparison of two hypotheses. The system returns a number from the interval <0, +∞>, which expresses how many times more likely the data are under one hypothesis than the…

Speech Analytics

Relevance: 5%      Posted on: 2018-04-06

Overview Phonexia Speech Analytics allows you to understand the  content of audio without having to listen to it. The results help both commercial entities and security/defense forces for immediate precise decision and response. The technologies reveal automatically WHAT content, TOPIC and KEY PHRASES are spoken, and many other metadata.   Speech Analytics - Typical Use-Cases Speech transcription is used in various application. Knowledge of content of whole call is bringing business value to the customer, comparing to listening the audio files by analytic or supervisor. Reading the text is also faster than listening the audio. Speech Analytics output is often…

Threshold

Relevance: 5%      Posted on: 2018-04-04

Number defining how much the score of the found word must be to appear among detections.

Speech Quality Estimator – Essential

Relevance: 5%      Posted on: 2018-04-04

Phonexia’s Speech Quality Estimator quantifies the acoustic quality of recordings. This helps the user to quickly determine whether the acoustic quality of a recording is good for processing with other speech technologies or not. As an answer for SQE, the SPE returns a json/xml file. This file includes general information about the technology and statistics of all (one or two) channels. The statistics of all channels include the numbers for many aspects of recording quality, and the overall global score. Technology The technology is language-, accent-, text-, and channel- independent Compatibility with the widest range of audio sources possible (applies…

Phonexia technology models EoL

Relevance: 5%      Posted on: 2018-07-11

Information about release dates, support and maintenance periods of Phonexia technology models.

Supported audio formats

Relevance: 5%      Posted on: 2018-12-10

Supported audio format are: WAVE (*.wav) container including any of: unsigned 8-bit PCM (u8) unsigned 16-bit PCM (u16le) IEEE float 32-bit (f32le) A-law (alaw) µ-law (mulaw) ADPCM FLAC codec inside FLAC (*.flac) container OPUS codec inside OGG (*.opus) container   Other audio formats must be converted using external tools. SPE server can be configured to support automated conversion on background, see SPE configuration hints. Great tools for converting other than supported formats to supported are ffmpeg (http://www.ffmpeg.org) or SoX (http://sox.sourceforge.net/). Both are multiplatform software tools for MS Windows, Linux and Apple OS X. Example of usage: ffmpeg ffmpeg -i <source_audio_file_name>…

Contact

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

Visit Us at Address: Chaloupkova 3002/1a, CZ 612 00 Brno, Czech Republic, European Union GPS: N 49° 13.426', E 016° 35.898 General Queries and Sales info@phonexia.com landline: +420 511 205 265 Technical Support support@phonexia.com landline: +420 511 205 265 cellphone: +420 731 810 643 Company registration details Identification number (ICO): 27680258 VAT identification (DIC): CZ27680258 Registered in the Business Register kept at the District Court in Brno, File C, Inset 51524.

Keyword Spotting results explained

Relevance: 5%      Posted on: 2019-06-12

This article aims on giving more details about Keyword Spotting outputs and hints on how to tailor Keyword Spotting to suit best your needs. Scoring Keyword Spotting works by calculating likelihoods that at a given spot occurs a keyword or just any other speech, and comparing those two likelihoods. The following scheme shows Background model for anything before the keyword (1), the Keyword model (2) and a Background model of any speech parallel with the keyword model (3). Models 2 and 3 produce two likelihoods – Lkw and Lbg (any speech = background). Raw score is calculated as log likelihood…

AVG

Relevance: 5%      Posted on: 2018-02-01

Average value – An average is the sum of a list of numbers divided by the number of numbers in the list

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

Relevance: 5%      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…

LR

Relevance: 5%      Posted on: 2018-02-01

Likelihood Ratio – Result from statistical test for two models comparation. It gives back number which expresses how many times more likely the data are under one model than the other. LR meets numbers in interval <-∞;+∞>

Phonexia – introduction

Relevance: 5%      Posted on: 2018-03-14

What we believe in At Phonexia, we find joy in pushing the boundaries of innovation in the field of speech technology by automating and simplifying solutions for many of today’s complex communication and security-strategic challenges. By providing our partners and customers with state-of-the art speech-technology software, we leverage the power, and data, in their voices. Who we are Phonexia is the only speech technology software manufacturer that reveals and leverages the most data in speech for enterprising trailblazers across the globe who want to discover and develop powerful new skills in a knowledge-based economy. We have more than 19 years…