Search Results for: model customization

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STT Language Model Customization tutorial

Relevance: 100%      Posted on: 2019-04-24

Language Model Customization tool (LMC) provides a way to improve the Speech To Text performance by creating customized language model. Language model is an important part of Phonexia Speech To Text. In a simplified way it can be imagined as a large dictionary with multiple statistics. The Speech To Text technology uses this dictionary and statistical model to convert audio signals into the proper text equivalents. Due to general diversity of spoken speech, the default generic language model may not acknowledge the importance of certain words over other words in certain situations. Language model customization is a way to inform the…

Packages, Updates vs. Upgrades

Relevance: 9%      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…

Speech To Text

Relevance: 9%      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…

Age Estimation

Relevance: 5%      Posted on: 2018-04-12

Phonexia Age Estimation (AGE) estimates the age of a speaker from audio recording. The process of voiceprint extraction is similar to the extraction of SID, but as a result different features get extracted; therefore, the voiceprints extracted from AGE and SID are not mutually compatible. 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…

Phonexia Speech Platform

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

  Phonexia Speech Platform (Speech Platform) provides partners a complete portfolio of speech technologies with an easy-to-use design. The platform allows users to design and deploy a wide range of speech processing systems in a short time and without extensive knowledge of the technologies background. Products On top of Speech Platform, several products provided: for commercial market Phonexia Speech Analytics Phonexia Voice Biometrics for government market Phonexia Speech Analytics GOV Phonexia Voice Biometrics GOV Characteristics Completeness – all speech technologies in one place Simple to use – RESTfull API for rapid development Modularity – build your own specific process workflow…

TUTORIAL: Speaker Identification – How to Do a Basic Test

Relevance: 5%      Posted on: 2019-10-08

Phonexia Speaker Identification is a voice biometry tool for recognition of speakers by their voice. In this video, we will show you how to start using this technology! You will learn how to create a "Speaker Model" to identify a speaker in a set of data. Ready to test it? Start with our video: What else is needed? 1. Phonexia Evaluation Package Evaluation package (download page) is consisting of Phonexia Browser and Phonexia Speech Engine including all necessary technologies. 2. Data We prepared the dataset for your testing. Package contains data for speaker model creation and speaker spotting too. The…

LM

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

Language Model (“vocabulary” in STT technology)

Voice Inspector

Relevance: 5%      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…

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…

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 <-∞;+∞>