What is speech recognition?

Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition, or speech-to-text, is a capability which enables a program to process human speech into a written format. While it’s commonly confused with voice recognition, speech recognition focuses on the translation of speech from a verbal format to a text one whereas voice recognition just seeks to identify an individual user’s voice.

Key features of effective speech recognition

Many speech recognition applications and devices are available, but the more advanced solutions use AI and machine learning. They integrate grammar, syntax, structure, and composition of audio and voice signals to understand and process human speech. Ideally, they learn as they go — evolving responses with each interaction.

The best kind of systems also allow organizations to customize and adapt the technology to their specific requirements — everything from language and nuances of speech to brand recognition. For example:

  • Language weighting: Improve precision by weighting specific words that are spoken frequently (such as product names or industry jargon), beyond terms already in the base vocabulary.
  • Speaker labeling: Output a transcription that cites or tags each speaker’s contributions to a multi-participant conversation.
  • Acoustics training: Attend to the acoustical side of the business. Train the system to adapt to an acoustic environment (like the ambient noise in a call center) and speaker styles (like voice pitch, volume and pace).
  • Profanity filtering: Use filters to identify certain words or phrases and sanitize speech output.

 

Speech recognition use cases

A wide number of industries are utilizing different applications of speech technology today, helping businesses and consumers save time and even lives. Some examples include:

Automotive: Speech recognizers improves driver safety by enabling voice-activated navigation systems and search capabilities in car radios.

Technology: Virtual assistants are increasingly becoming integrated within our daily lives, particularly on our mobile devices. We use voice commands to access them through our smartphones, such as through Google Assistant or Apple’s Siri, for tasks, such as voice search, or through our speakers, via Amazon’s Alexa or Microsoft’s Cortana, to play music. They’ll only continue to integrate into the everyday products that we use, fueling the “Internet of Things” movement.

Healthcare: Doctors and nurses leverage dictation applications to capture and log patient diagnoses and treatment notes.

Sales: Speech recognition technology has a couple of applications in sales. It can help a call center transcribe thousands of phone calls between customers and agents to identify common call patterns and issues. Cognitive bots can also talk to people via a webpage, answering common queries and solving basic requests without needing to wait for a contact center agent to be available. It both instances speech recognition systems help reduce time to resolution for consumer issues.

Security: As technology integrates into our daily lives, security protocols are an increasing priority. Voice-based authentication adds a viable level of security.

What is Speech Analytics?

Speech analytics can be defined in many different ways, but should be primarily understood as a specialized technology that enables the process of extraction of relevant data from a real-time conversation or a recorded audio file.
This technology automates a range of operational processes, including: call recording, speech recognition and transcription, search by keywords and extraction of actionable insights, conversation quality management, behavioral analysis, and other forms of data analysis.

With the aid of speech analytics solutions, all of these processes are carried out at a higher speed and at a more sophisticated level than by any human-powered data mining system.

In fact, in the context of a modern-day front office, the manual collection and analysis of information are hardly imaginable. This antiquated approach would certainly require the mobilization of significant human and financial resources, making the process terribly cost-inefficient. Call analytics benefits, on the other hand, are too compelling.