Search Results for: Language Model

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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…

LID adaptation

Relevance: 94%      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…

SPE3 – Releases and Changelogs

Relevance: 83%      Posted on: 2021-06-11

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). Releases Changelogs Speech Engine 3.40.5, DB v1700, BSAPI 3.40.4 (2021-05-09) Public release Fixed: When trying to register webhook over existing webhook for any stream technology, SPE returns HTTP 400 (1069) error instead of HTTP 500 Fixed: Invalid SQL syntax when overwriting voiceprint in a database Speech Engine 3.35.7, DB v1601, BSAPI 3.35.5 (2021-05-09) Public release Fixed: Invalid SQL syntax when overwriting voiceprint in a database Speech Engine 3.40.4, DB v1700, BSAPI…

Understanding SPE database

Relevance: 78%      Posted on: 2021-06-05

SPE database serves multiple purposes: stores SPE internal data stores various information about SPE entities created by SPE user audio files metadata speaker models and their voiceprints speaker groups and their voiceprints calibration sets keyword lists language packs audio source profiles stores cached processing results (optional, can be set in SPE configuration file) stores SPE log data (optional and MySQL only, can be set in SPE configuration file) To cache or not to cache? Well, that's a question... ;-) It depends on the particular use case AND on the design of your application, whether using the built-in results caching would be…

SPE configuration file explained

Relevance: 61%      Posted on: 2021-05-03

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…

Understanding SPE directory structure

Relevance: 59%      Posted on: 2021-05-15

Good understanding of SPE directory structure helps to better understand the inner workings of SPE and simplifies troubleshooting. It's also useful for expert-level tuning of parameters of individual technologies and optimizing SPE configuration e.g. for deployments with shared resources, or deployments in virtualized environments, etc. The SPE directory structure looks like this (the tree depth is limited for better readability): {SPE_installation_directory} ├── bsapi │ ├── age │ │ ├── data │ │ ├── example . . └── settings . . . . │ └── vad │ ├── data │ ├── example │ └── settings ├── data │ ├── benchmark │…

Language Identification (LID)

Relevance: 49%      Posted on: 2021-02-25

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. Application areas Preselecting multilingual sources and routing audio files to language-dependent technologies (transcribing, indexing, etc.) Analyzing network traffic media (language statistics) Routing particular calls (languages) to human operators (language experts) Recognized languages Languages pre-trained in the default language pack are listed in the table below, each LID generation is a separate column (in the 4th generation we switched to using language

Speech To Text

Relevance: 46%      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…

Terminology

Relevance: 24%      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…