…20 hours of audio is required, see requirements below Enhancing existing language model by adding your own audio files to existing built-in language at least 5 hours of audio is required, see requirements below Creating custom language pack consisting of your chosen set of languages, both pre-trained or created from your audio files Audio recordings requirements Format: WAV, FLAC, RAW…
Search: Time%20%20%20%20%20ysis%20Extractor
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A: Signal-to-Noise Ratio (SNR) is an important metric of whether a recording is worth further processing by other speech technologies, so it is part of our Speech Quality Estimation. However, calculating SNR automatically is not a trivial task. We use the fact that the statistical distribution of the frequencies in the waveform of speech has Gamma distribution. In contrast, noise…
Phonexia requires your acceptance of The End User Agreement before downloading, please check it. Step #1 – Download the package This package allows new users to try and evaluate Phonexia Voice Inspector. Phonexia Voice Inspector 5.2.0 for Linux 64-bit 281.9 MB Download Phonexia Voice Inspector 5.2.0 for Windows 64-bit 294.62 MB Download The package contains the following components, technologies &…
…✓ Voice Activity Detection (VAD) ✓ ✓ Time Analysis Extraction (TAE) ✓ ✓ Speech Quality Estimation (SQE) ✓ ✓ Language Identification (LID) ✓ Gender Identification (GID) ✓ Age Estimation (AGE) ✓ Speaker Diarization (DIAR) ✓ Results caching Processing results can be optionally stored in results cache database to speed up eventual re-processing of the same recordings by the same technology…
About Main idea of the Phonexia Academy is to help partners to understand the market, Phonexia’s products and technologies. Sell more, deliver your projects on time and at the highest quality, and support your clients effectively. We provide following trainings: Phonexia technologies introduction (online video course) Technical Training Essentials (online video course) Technical Training Advanced – 2 courses: Voice Biometrics…
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…
A: Similarly as human, the ASR (STT) engine is doing the adaptation to an acoustic channel, environment and speaker. Also the ASR (STT) engine is learning more information about the content during time, that is used to improve recognition. The dictate engine, also known as on-the-fly transcription, does not look to the future and has information about just a few…
…XL5 Diarization (DIAR) – model XL4 Language Identification (LID) – model L4 Gender Identification (GID) – model XL5 Age Estimation (AGE) ) – model XL5 Voice Activity Detection (VAD) – model GENERIC_3 and SID4_XL5 Speech Quality Estimation (SQE) Time Analysis Extraction (TAE) Waveform Denoiser (DENOISER) Phonexia Browser example audio (in ./BROWSER/example/ and ./SPE/bsapi/{technology}/example/) Step #2 – First start To get…
…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…
…from the preferred phrases and interpolate it in realtime with the generic language model: P(word|history) = Pgeneric(word|history) + αPpreferred(word|history) The preferred words and phrases are favored, while retaining the existing accuracy on common text. Preferred phrases in Speech Engine Use POST /technologies/stt or POST /technologies/stt/input_stream call to start transcription with a list of preferred phrases. To be precise, these actually…
…software cannot remove unwanted speech or music in the background. Denoiser is used to remove noise from the recording and at the same time to amplify the speech signal for: Better intelligibility when listening by people (recommended use), Achieving better results with automatic speech recognition technologies (necessary to test on customer data first). Input: audio file (format details – see…
…other” accounts still need to register the file to be able to actually use it in SPE… otherwise, the file would be visible only by the account which originally uploaded the file. This is because SPE keeps some file metadata (name, timestamps, …) in its database and files not having its database record (associating them with the SPE account) are…
…i.e. should be handled by the application built on top of the SPE API. This includes handling of any metadata associated with the processed audiofiles, like phone numbers, source of the recording, date/time the audio was recorded, references to the persons speaking in the recording (names, photos, …), languages spoken in the recording, etc. – all this data is expected…
…i.e. you can specify only preferred phrases, or only add words to dictionary, or use both features at the same time. Example of input for starting transcription, specifying two preferred phrases and two words to be added (one with explicitly specified pronunciation): { “preferred_phrases”: { “phrases”: [ { “phrase”: “this is preferred phrase” }, { “phrase”: “some other phrase” },…
MODULE 4: Speech Analytics technologies (23 min) Common generic rules for CLI, REST and GUI Speech To Text (STT) in CLI, REST and GUI Keyword Spotting (KWS) in CLI, REST and GUI Phoneme Recognizer (PHNREC) in CLI, REST and GUI Time Analysis Extraction (TAE) in CLI, REST and GUI Summary https://www.youtube.com/watch?v=-FAoRywqv7U…