Search Results for: reaction times

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Keyword Spotting (KWS)

Relevance: 100%      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…

Speech To Text

Relevance: 100%      Posted on: 2019-05-27

Phonexia Speech To Text – also known as a voice-to-text or speech recognition – converts speech signals into plain text. After the conversion, text can be easily read, edited, searched, processed by text-based data mining tools or archived. Phonexia Speech To Text is optimized for noisy recordings and colloquial speech, can process audio files as well as audio streams and can provide results in several output formats. Typical use cases look for specific information in large call archives (e.g., claims inspection) get additional value by advanced analysis of call traffic (e.g., topic detection) maintain short reaction times by routing calls…

Time Analysis (TAE)

Relevance: 100%      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: 100%      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…

Keyword Spotting

Relevance: 100%      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…

Software Vetting

Relevance: 50%      Posted on: 2018-04-06

The purpose of this document is to help client to satisfy their high security standards during integration of Phonexia software to their critical infrastructure. The vetting ensures that Phonexia software is not dangerous to the client’s infrastructure in any way. It means there are no backdoors, viruses, worms, Trojan horses, spyware, adware, critical bugs, unwanted functionality, no information is sent outside the client’s infrastructure. Vetting context Speech technology is a very dynamic area with a very fast development. For example the speaker identification error rate decreases to half between each two evaluations organized by National Institute of Standards and Technology,…

Voice Inspector

Relevance: 50%      Posted on: 2017-05-18

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 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…

Software Vetting (Best Practice)

Relevance: 50%      Posted on: 2017-06-15

The purpose of this document is to help client to satisfy their high security standards during integration of Phonexia software to their critical infrastructure. The vetting ensures that Phonexia software is not dangerous to the client’s infrastructure in any way. It means there are no backdoors, viruses, worms, Trojan horses, spyware, adware, critical bugs, unwanted functionality, no information is sent outside the client’s infrastructure. Vetting context Speech technology is a very dynamic area with a very fast development. For example the speaker identification error rate decreases to half between each two evaluations organized by National Institute of Standards and Technology,…

Keyword Spotting results explained

Relevance: 50%      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 and results explanation 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…

Q: What LLR, LR and score mean?

Relevance: 50%      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…

Speech To Text results explained

Relevance: 50%      Posted on: 2019-05-27

This article aims on giving more details about Speech To Text outputs and hints on how to tailor Speech To Text to suit best your needs. In the process of transcribing speech, the Speech To Text technology usually identifies multiple alternatives for individual speech segments, as multiple phrases can have similar pronunciations, possibly with different word boundaries, e.g. “eight tea machines” vs. “eighty machines”. The technology provides several types of output to show only one or more transcription alternatives. One-best output 1-best output provides transcription containing only the highest-scoring words. Each segment provides information about the transcribed word itself, the…

LR

Relevance: 50%      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 <-∞;+∞>

Voice Inspector – Interpretation of results

Relevance: 50%      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 Voice Inspector v3

Relevance: 50%      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…

Speaker Identification (SID)

Relevance: 50%      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.…

Terms of Service

Relevance: 50%      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…

Phonexia Ethical Code

Relevance: 50%      Posted on: 2018-03-24

Application of the Code It is the policy of Phonexia, s.r.o. (“Phonexia”, “we”) to maintain the highest level of ethical standards in the conduct of our business affairs. Our values guide our actions in all cases. The actions and conduct of our officers, directors and employees (collectively, “Phonexia personnel”), as well as others acting on our behalf, are essential to maintain these standards and promote highly ethical reputation of Phonexia. To that end, all our personnel including agents, consultants and contractors as well as distribution partners involved in Phonexia´s international business activities must read, become familiar and comply with this…