Speech Recognition
Speech recognition is the subfield of computational linguistics which incorporates knowledge and research in the linguistics, computer science, and electrical engineering fields to develop methodologies and technologies that enables the recognition and translation of spoken language into text by computers and computerized devices such as those categorized as Smart Technologies and robotics. It is also known as "automatic speech recognition" (ASR), "computer speech recognition", or just "speech to text" (STT).
We at ASRlytics makes use of State-of-Art Neural Networks for Speech Recognition and Time Alignments. Our engine can accurately recognize spoken words and convert speech into text. It also supports standard telephony as well as web and mobile applications. Being capable of actioning voice commands given to electronic devices such as computers, tablets, smartphones or telephones, our Speech Recognition engine can work in different applications.
Our engines can be modified easily as per the needs, we also have a Server/Client architecture and is designed to be consumed on-premises, on-device or in the cloud and we already has our applications available in Web, Android, IOS. We can see the usage in diverge fields like Pronunciation Assessment, Language learning, Call Analytics etc.,
Highly customisable
Quick initial transition
Acoustic models
Quality transcriptions
Audio/Speech Analytics
Audio analytics or speech analytics refers to the extraction of information and meaning from audio signals for analysis of data , classification, storage, retrieval, synthesis, etc.It is a task of finding the best-matching speaker for unknown speaker from a database of known speakers. It is mainly a part of the speech processing, stemmed from digital signal processing and the Speaker Independent system enables people to have secure information and property access.
The task of Audio analytics involves identifying different segments in broadcast audio like pure speech, speech to text with background speech and pure speech and further classifying them into their respective classes. Pure speech generally contains a lot of information regarding a particular event.
Hence, a speaker independent speech-to-text transcription is a necessary first step to extract keywords or event tags. The presence of a particular keyword in a speech segment relating to a particular event may be obtained by performing keyword spotting in the continuous speech segment. The textual information, so extracted will be a useful source for text mining
Keyword Spotting
Keyword spotting Identifies spoken phrases from the audio that match specified keyword strings with a user-defined level of confidence. This feature is especially useful when individual words or topics from the input are more important than the full transcription.
This technology is highly helpful when we need to find audio files having a particular keyword, this method works well in that case, instead of transcribing all the calls it will just look for the audio files that have the keyword.