What is Speech Recognition? What are its Applications?
Speech recognition, also known as speech to text, is the ability of a machine or computer program to identify spoken words and convert them into readable text. Rudimentary forms of speech recognition software will only be able to recognize a limited range of vocabulary and phrases, while more advanced versions will be able to pick up complex speech in a variety of languages, accents, and dialects. Speech recognition is at the intersection of computer engineering, linguistics, and computer science. Many smartphone and computer devices on the market today come with some form of speech recognition technology built into their software.
It is important to note that while many people may use voice recognition and speech recognition as two interchangeable terms, they are in fact two distinct processes. While speech recognition is used to identify words in a particular spoken language, voice recognition aims to identify a speaker’s individual voice by using biometric technology. Moreover, speech recognition enables the hands-free control of various devices and equipment, creates print-ready diction, and gives input to auto-translation. Speech recognition is also used to enable popular personal assistants in smartphones and devices such as Apple’s Siri or Amazon’s Alexa.
How does speech recognition work?
Speech recognition works by using algorithms through a process referred to as language and acoustic modeling. Acoustic modeling is used to represent the relationship between audio signals and linguistic units of speech. Contrarily, language modeling matches sounds together with word sequences to help distinguish between similar-sounding words or phrases. Additionally, Hidden Makarov models or HMMs are often used to recognize certain temporal speech patterns and in turn improve accuracy within the system. An HMM is a statistical model that represents a randomly changing system, where it is assumed that future changes will not be dependent upon past changes.
Other methods used in speech recognition are natural language processing and N-grams. Natural language processing or NLP makes the overall speech recognition process easier and takes less time to institute. Alternatively, N-grams provide a relatively simple approach to language models and work by creating a probability distribution for a particular sequence. Finally, the most advanced speech recognition software will make use of state-of-the-art AI and machine learning technology.
What are the key features of effective speech recognition?
Many top-of-the-line speech recognition software options will allow users to adapt and customize the technology to their specific needs and requirements. Whether it be brand recognition or the nuances of a foreign language or speech, these software options make use of grammar, syntax, structure, and compositions of voice and audio signals to understand and process human speech. Examples of some of these features include:
- Language weighting – language weighting improves precision by weighting specific words that are spoken frequently (such as industry jargon or the name of a specific product) beyond terms used in everyday language.
- Speaker labeling – speaker labeling outputs a transcription that tags or cites a speaker’s individual contribution to a conversation with multiple participants
- Acoustics training – acoustics training will enable the system to adapt to an acoustic environment such as the ambient noise in a busy office setting. Furthermore, it will also pick up speaker styles like pace, volume, and voice pitch.
- Profanity filtering – profanity filtering can be used to identify and censor certain words in an attempt to sanitize speech output.
What are the applications for speech recognition?
The most frequent application of speech recognition today is for use in mobile devices. From voice dialing to asking Siri what the weather will be like on the upcoming Monday, voice recognition has become a key feature of many smartphone offerings currently on the market. Voice dialing, speech-to-text processing, call routing, and voice search features also function based upon speech recognition technology. Speech recognition can also be found in computer word processing programs such as google docs or Microsoft word, where users can change and dictate what they want to show up as text.
In the context of redaction software, speech recognition is used to automatically transcribe audio and video files. Products such as CaseGuard Studio will allow users to automatically transcribe hours of video and audio files in a matter of minutes. Moreover, this can be done in dozens of different languages with a multitude of stylistic choices. For instance, you may want to change the font or background color for the text on your transcription and captions as it appears in an online video.