Tech Term Decoded: Speech Analytics

Definition

Speech analytics, also referred to as interaction analytics, is a technology that utilizes artificial intelligence to grasp, process, and examine human speech. Call recordings and transcripts from digital channels such as chat and text messages can now be analyzed by contact centers via the use of speech analytics. With speech analytics software having the capability to analyze 100% of contacts 24/7, contact centers can be more forward thinking and have a clearer understanding of what happens during customer interactions.

Speech analytics makes it possible for the examination of customer interactions in order to get meaningful insights and customer sentiment by transforming spoken words into structured data points. Using natural language processing (NLP), automatic speech recognition (ASR), machine learning, and artificial intelligence (AI), it extracts customer preferences, behavior, and emotions from customer conversations [1].

For instance, imagine a construction contractor in Enugu calling Dangote cement customer service about cement quality:

Customer: "Oga, this cement wey I buy from una last week no dey set well well. My building work don delay because of am. I need better quality cement sharp sharp!"

The AI speech analytics gives the output:

Issue Type: Product quality complaint

Location: Enugu region identified

Urgency: "Sharp sharp" indicating immediate need

Impact: Construction delay i.e. high business impact

Tone: Professional but frustrated

 

It then gives an Automated Response:

Quality control team alerted about Enugu batch

Customer flagged for priority replacement

Regional sales manager notified

Complaint logged for batch investigation

This prevents wider quality issues by identifying problematic cement batches early through voice pattern analysis of customer complaints.

Different phases of Speech Analytics process [2].

Origin

The origin of speech analytics dates back to the early 1960s, with the advent of the first automatic speech recognition systems, which unfortunately had limited accuracy and were specially made for controlled environments. However, over the decades, technological advancements have significantly enhanced the capabilities of speech recognition, setting the scene for more sophisticated speech insights analysis.

Towards the end of the 1990s, the integration of machine learning and natural language processing transformed the field. Businesses started seeing the value of analyzing customer interactions for insights, which resulted to the rise of speech analytics as a distinct area of study [3].

Context and Usage

AI speech analytics can be applied in various fields across industries and scenarios to enhance service. For instance, the technology can be implemented for customer service and support, sales and marketing, market research and consumer insights and cost savings.

In Customer service and support, by deducing customer’s sentiments from their voice in calls, businesses can cater and address customer needs with a better understanding from the start, allowing customer support agents to provide feedback and solutions in real-time.

In Sales and marketing, by leveraging access to a customer’s behaviors and emotions from prior calls, agents can be better trained for future scenarios, leading to more efficient sales calls.

When it comes to market research and consumer insights, Speech analytics can be used to listen to customer feedback to improve product development.

For cost savings, if there are repetitive concerns on behalf of customers, a business may opt to resolve them via self-service, saving costs for call centers and reducing delays in issue resolutions [4].

Why it Matters

Every conversation in a contact center is a gold mine of customer insights which needs to be tapped into. It is almost impossible for human agents to capture every detail within thousands of interactions happening daily. Speech analytics converts raw voice data into actionable insights in real-time. By automatically analyzing call content, identifying trends, and even detecting customer sentiment, speech analytics empowers businesses to turn routine interactions into opportunities for growth [5].

In Practice

A real-life case study of a company practicing AI speech analytics can be seen in the case of Enthu. Their business solution answered a simple yet powerful question; how can businesses better understand their customers by listening, analyzing, and acting on the voices that matter the most. Enthu.AI's advanced speech analytics empowers consumer brands to decode sentiment and auto-monitor quality seamlessly.

See Also 

Related NLP and Text Processing terms:
Speech Recognition: Technology that converts spoken words into written text
Syntax Analysis: Understanding sentence structure 
Text Analytics: Deriving insights from text 
Text Summarization: Condensing content automatically 
Tokens: Individual units (words, subwords, characters) that text is divided into for processing 

References

1. Nice. (2025). Speech analytics.
2. Observe. (2025). Speech Analytics for Contact Centers.
3. Williams, B. (n.d). Speech Analytics Research: In-Depth Analysis.
4. Amit, J. (2024). AI Speech Analytics: How it Works and What it Does.
5. Zoom. (2025). What is speech analytics and how does it work?