Search Results for: speaker identification results

Results 1 - 10 of 64 Page 1 of 7
Results per-page: 10 | 20 | 50 | 100

Speaker Identification: Results Enhancement

Relevance: 100%      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,…

Speaker Identification (SID)

Relevance: 70%      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.…

Language Identification results explained

Relevance: 67%      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.…

TUTORIAL: Speaker Identification – How to Do a Basic Test

Relevance: 67%      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…

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

Relevance: 64%      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…

Voice Inspector – Interpretation of results

Relevance: 39%      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…

Gender Identification

Relevance: 36%      Posted on: 2018-04-16

Gender Identification is a language-, domain- and channel-independent technology that uses the acoustic characteristics of the recording to determine the gender of the speaker in question. This technology is able to distinguish between two genders: Male (M) and Female (F). Minimum of speech signal for identification: 9+ sec recommended Output scoring: likelihood ratio and percentage metric (0-100%) Typical use cases: filtering calls by gender, playing advertisement focused on specific gender, getting quick demographic analysis of the recordings. The speed of Gender Identification is up to 150 FtRT (depending on the model).

Speech To Text results explained

Relevance: 36%      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…

Language Identification (LID)

Relevance: 36%      Posted on: 2019-05-20

Phonexia Language Identification (LID) will help you distinguish the spoken language or dialect. It will enable your system to automatically route valuable calls to your experts in the given language or to send them to other software for analysis. Phonexia uses state-of-the-art language identification (LID) technology based on iVectors that were introduced by NIST (National Institute of Standards and Technology, USA) during the 2010 evaluations. The technology is independent on any text, language, dialect, or channel. This highly accurate technology uses the power of voice biometrics to automatically recognize spoken language. Application areas Preselecting multilingual sources and routing audio streams/files…

Keyword Spotting results explained

Relevance: 33%      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…