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The technology in Engage Analyzes makes it possible to audio mine up to 100 percent of calls, in real rime if desired compared with three to five percent of calls with LVCSR without the masive computing power in the older systems.
Pre-processing or indexing of content is typically 60-80 times faster than real time, more than an order of magnitude faster than Large Vocabulary Conversational Speech Recognition (LVCSR) speech-to-text systems.
For example, in automatic speech-recognition systems the information can be used for unsupervised speaker adaptation [1, 15], which can significantly improve the performance of speech recognition in large vocabulary continuous speech recognition (LVCSR) systems [10, 28, 4].
Woodland, "Unsupervised training and directed manual transcription for LVCSR", in Speech Communication, Vol.
The two most common approaches are phonetic-only processing and Large Vocabulary Continuous Speech Recognition (LVCSR).
The first is to choose one of two SA approaches: large-vocabulary conversational-speech recognition (LVCSR) versus phoneme-based SA.
"Their ability to process call content in order of magnitude faster than LVCSR systems makes analyzing 100 percent of large call volumes a practical proposition."
For Hungarian at the present time there exists no general-purpose large vocabulary continuous speech recognizer (LVCSR).
Inspired by the activities within the DARPA research community, we have been developing a large-vocabulary, continuous-speech recognition (LVCSR) system for Japanese broadcast news speech transcription .
Chung, "Automatic Generation of Concatenate Morphemes for Korean LVCSR," Journal of the Acoustical Society of Korea, vol.
There are three major types of speech analytics engines: phonetics, large vocabulary continuous speech recognition (LVCSR), and direct phrase recognition.
Speech-to-text offerings are based on a large vocabulary continuous speech recognition (LVCSR) engine and translate recorded audio into searchable text.
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