…performance precisely, it’s important to prepare evaluation recordings set very carefully. The requirements are: 50+ known speakers, 200+ recordings in total (i.e. 3 to 5 recordings per speaker*) 1+ minute of net speech in each recording (i.e. usually 2+ minutes recording length) only one speaker in each recording wide variety of gender and age is recommended recordings should be as…
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…only English models for Speech To Text and Keyword Spotting. Additional supported languages are available upon request. ⓘ Click to show/hide the package content Speech Engine – technologies included: Speech To Text (STT) – model EN_US_6 (US English) Keyword Spotting (KWS) – model EN_US_6 (US English) Phoneme Recognizer (PHNREC) – model EN_US_6 (US English) Speaker Identification 4 (SID4) – model…
…and their usages Filtering and supporting technologies 04:32 Speech Quality Estimation (SQE) 05:27 Voice Activity Detection (VAD) 06:37 Diarization (DIAR) 07:41 Age Estimation (AGE) 08:14 Waveform Denoiser Voice Biometrics technologies 08:56 Speaker Identification (SID) 10:18 Language Identification (LID) 11:10 Gender Identification (GID) Speech Analytics technologies 11:43 Speech Transcription (STT) 12:30 Keyword Spotting (KWS) 13:32 Phoneme Recognition (PHNREC) 13:54 Time Analysis…
MODULE 3: Voice Biometrics technologies (23 min) Common generic rules for CLI, REST and GUI Speaker Identification (SID) in CLI, REST and GUI Language Identification (LID) in CLI, REST and GUI Gender Identification (GID) in CLI, REST and GUI Summary https://www.youtube.com/watch?v=AyEoPfYVel8…
…FAQ Voice Inspector Permalink Q: What are the requirements for SID evaluation dataset? For evaluating the real life scenario of Phonexia Speaker Identification technology, the system needs to be calibrated by SID dataset. SID dataset (minimum requirements): To measure SID performance precisely, it’s important to prepare evaluation recordings set very carefully. The requirements are: 50+ known speakers, 200+ recordings in…
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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 sound of the spoken language, not sound of the speaking person) Languageprint archive (*.lpa file) – multiple languageprints combined into single archive Languageprint archives come pre-created…
…models VIN application (graphical user interface, GUI) with the following technologies in-build Speaker Identification (SID4_XL5) Speaker Diarization (DIAR) Voice Activity Detection (VAD) Speech Quality Estimator (SQE) Phoneme Recogniser (PHNREC) example population sets and audio (in ./examples/) and example report templates (in ./templates/) Hardware requirements minimum – CPU: Intel® Core™ i5, RAM: 4 GB, Required HDD space: 0.5 GB for software…
…of speech technologies (precision of speaker identification, transcription accuracy, etc.). Therefore it is essential to have as clean audio as possible. ? DO’S ? DON’TS Capture the sound as close to the source as possible, i.e. as close to the speaker’s mouth as possible as close to the recording source as possible to minimize the amount of ambient sounds and…
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…in our example is 36 seconds. After stripping silence, it gets 14 seconds – this means that original audio contains 38% of net speech and 62% of silence. Phonexia speech technologies analyze the entire recording, but pick only the speech segments for AI processing, i.e. the absolute processing time will be practically the same… Creating voiceprint by Speaker Identification took:…
…for individual models settings BSAPI configuration files (*.bs) and optionally manually created user configs (*.bs.usr) There is one exception – LID – which has additional two directories containing pre-built languageprint archives (*.lpa) and language packs: lprints and models. Schemes below show examples of directories for GID (Gender Identification), STT (Speech To Text) and LID (Language Identification): – GID and LID…