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Speech Analytics

11 min read

Speech Analytics

January 12, 2024

Decoding the Power of Words: A Detailed Overview of Contact Center Speech Analytics

Decoding the Power of Words: A Detailed Overview of Contact Center Speech Analytics

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From its fundamental role in transcribing voice calls using cutting-edge AI technology to its far-reaching benefits for telecom companies, we've explored the multifaceted landscape of this invaluable tool in this article.

The development of speech analytics applications presents numerous challenges that demand robust software engineering and machine learning expertise. At byVoice, our AI engineers have practical experience gained on multiple projects and research focused on voice applications. 

If you have a project idea involving speech recognition or related technologies, please reach out to us for a detailed discussion of the specifics.

Key Features to Consider When Choosing a Speech Analytics Tool

It is important to select a solution designed specifically for contact centers, which takes into account noise, poor audio quality, and other factors. A good solution will also be able to leverage Natural Language Processing (NLP) that is often used for such purposes as answering questions, classifying texts, and resolving information retrieval problems. With NLP, customers can use natural language to interact with automated systems.

1. Designed for contact centers

Avoid inflexible "black box" solutions lacking adaptability or updates. Observe.AI employs its proprietary AI to recognize and update vital business phrases, including trademarks and compliance terms. Identifying these terms is crucial for speech analysis, enhancing transcription accuracy. Inaccurate transcriptions yield useless or potentially harmful insights that impact compliance adherence when applied across numerous calls.

2. Customized recognition of business terms

Your solution should analyze the tone, pitch, and other vocal cues in customer interactions to identify and measure the emotional content. This feature helps businesses assess customer sentiment, understand emotional responses, and adjust their customer service strategies accordingly.

3. Detecting emotions

Your solution must stay adaptable, incorporating regular transcription improvements and an easy feedback loop for sustained high accuracy.

4. Quality enhancement

When looking to implement a speech analytics solution, you should prioritize four key features:

Natural Language Generation (NLG) is essential here. As a subset of NLP, NLG enables AI to generate real-time notes and suggestions for agents during conversations, summarize calls, and create detailed post-call summaries, alerts, and training contributions.

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Discover More Valuable Insights in the Field of Speech Analytics

Attempting to develop a speech recognition system from scratch would be a mistake. Large companies invest years in this technology, and catching up with them is challenging. Additionally, maintaining a speech recognition system at the required level of quality demands constant investment.

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Similarly, a mistake would be to develop text analysis components from scratch. In the field of AI, changes have been occurring rapidly in recent years, and by the time the project is completed, everything may become outdated.

The optimal strategy is integrating and tuning ready-made AI components into your own RA (Recognition and Analysis) system. A good architect anticipates the possibility of replacing one component with another, allowing you to choose AI component providers and provide your users with options. For example, solutions from different speech recognition solution providers can vary significantly in quality depending on language and domain.

Ensuring the acquisition of conversation records in an optimal format will be essential. The recognition system will necessitate dual-channel conversation recordings in lossless formats such as WAV or FLAC. Alternatively, one might employ sophisticated mechanisms to access voice traffic through protocols like SIP, RTP, MGCP, or Megaco. But by adopting an integration approach, you can focus on developing the UI of your products and assisting your users in implementing RA into their business processes.

Implementing a ready-made tool for speech analytics



May lack specific features or customization options needed for unique business processes.

May require adjustments to existing workflows to accommodate the tool.

Integration with other in-house systems may be less seamless.

If the vendor discontinues the product, it could lead to a need for a replacement.

Rapid implementation, as the tool is already developed and tested.

Ready-made solutions have lower initial costs compared to developing from scratch.

Access to new features and improvements without the need for in-house development.

The vendor usually provides regular updates and support.


May lack specific features or customization options needed for unique business processes.

May require adjustments to existing workflows to accommodate the tool.

Integration with other in-house systems may be less seamless.

If the vendor discontinues the product, it could lead to a need for a replacement.

Developing a speech analytics tool from scratch



Development from scratch typically requires more time and financial resources. Additionally, initial investment may be higher compared to implementing a ready-made solution.

Development project requires a specialized team with speech analytics and software development expertise.

Ongoing maintenance and updates are solely the responsibility of your organization.

Bug fixes and feature updates may take longer to implement.

You have complete control over the features, functionality, and design, allowing you to tailor the tool to your specific requirements.

The tool can be customized to suit your organization's unique needs and processes.

Easier integration with existing systems and workflows, as the tool is built specifically for your environment.

You have the flexibility to add or modify features as your requirements evolve.


Development from scratch typically requires more time and financial resources. Additionally, initial investment may be higher compared to implementing a ready-made solution.

Development project requires a specialized team with speech analytics and software development expertise.

Ongoing maintenance and updates are solely the responsibility of your organization.

Bug fixes and feature updates may take longer to implement.

We have reached the most crucial part of this article. The logical conclusion from all that has been said above is that speech analytics is an indispensable tool. If you are a software provider for telecommunications companies, this tool should be among your offerings.

Tool Development vs. Out-of-the-Box Solutions: What to Choose

But what way will be the best fit for your business — to develop your own speech analytics tool or to integrate/implement a ready-made one? To figure out this question, let's consider the pros and cons of both options.

Let’s See What Your Business May Lose Without Speech Analytics

A B2B partnership is typically a win-win combination. However, it can quickly become a lose-lose situation if you cannot provide your customers with the tools their businesses require. Therefore, speech analytics is a monetizable tool that allows you and your customers to generate additional revenue.

Opportunities for upselling and cross-selling

As mentioned earlier, telecom companies recognize the value of a tool like speech analytics and are actively integrating it into their IT infrastructure. The providers may offer speech analytics as part of their solutions. Failure to provide this tool may create a competitive gap, making the provider less attractive to businesses seeking comprehensive telecom solutions.

Customer retention

The telecom industry is dynamic, with constant technological advancements and changing customer expectations. Speech analytics tools demonstrate a commitment to staying ahead of industry trends and adapting to evolving customer needs.

Adaptability to industry trends

Volume of processed phone calls

To grasp the significance of the speech analytics tool for your customers, consider a call center with thousands of operators handling hundreds or even thousands of calls daily. Managing such call volumes requires advanced technology and a proficient team of analysts. However, manual processing is limited to approximately 1-3% of calls, even with a skilled team.

When calls are processed manually, the true extent of problematic areas remains undisclosed.

Many modern telecom companies have long recognized the value of speech analytics and have actively integrated this tool into their operations.

If you don't provide a speech analytics tool to your customers, you may miss out on the following crucial benefits:

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Want to Know How to Implement Speech Analytics Into Your Business?

How Your Customers Can Benefit from Speech Analytics: TOP-6 the Most Popular Use Cases

Use case


Customer experience improvement

Analyzing customer calls to identify common pain points, concerns, and areas of dissatisfaction.

Enables businesses to proactively address customer issues, enhance service quality, and improve overall customer satisfaction.

Quality assurance

Evaluating agent performance by analyzing call interactions.

Provides insights into agent communication skills, script adherence, and overall performance, facilitating targeted training and coaching for improvement.

Sales optimization

Analyzing sales calls to identify successful strategies, customer objections, and areas for improvement.

Helps sales teams reconsider their approach, tailor pitches to customer needs, and enhance conversion rat.

Operational efficiency

Identifying bottlenecks and inefficiencies in call center processes.

Enables organizations to streamline workflows, optimize resource allocation, and enhance overall operational efficiency.

Market research

Analyzing customer conversations to gain insights into market trends and competitor analysis.

Helps organizations stay informed about market dynamics, customer preferences, and competitor strategies.

Employee training and development

Using speech analytics to identify training needs and areas for improvement among customer service agents.

Facilitates targeted training programs, leading to the continuous improvement of agent skills and performance.

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Learn More About This Project

Cloud Contact Center and Cloud PBX

1. A Cloud PBX records conversations into files.

2. We transmit this data to a speech recognition system (an external solution).

3. Then, the whole conversation is tagged based on user-defined rules.

4. Both the text and tags are put in storage, allowing users to perform searches based on tags and generate statistics on tag usage.

How byVoice implemented this: based on a real project

byVoice has been collaborating with a telecommunications industry leader that offers its customers high-quality communication services, including Cloud Contact Center and Cloud PBX with a built-in speech analytics tool. For this client, we have implemented the project of integrating solutions according to the following scenario:

How Does Contact Center Speech Analytics Work?

How is this implemented in practice?

Call processing using speech analytics tools can be divided into four main phases:

This is the initial phase where raw call data is recorded, collected, and prepared for analysis. It includes the following activities:

Data collection: Speech analytics tools capture audio recordings of phone conversations between customers and agents.

Data transcription: The recorded speech is transcribed into text, converting spoken words into a more easily analyzed format.

Data cleaning: The transcribed text undergoes cleaning to remove irrelevant information, such as background noise or non-speech sounds.

Data structuring: The cleaned and transcribed data is organized into a structured format, making it suitable for further analysis.

Sentiment analysis: Speech analytics tools analyze the tone and sentiment of customer and agent interactions to determine the overall mood of the conversation.

2. Data processing

1. Setup

The setup phase involves configuring the parameters and settings for initiating the analytics process. This includes defining when and how the analysis should occur, as well as specifying the scope of analysis (e.g., conversations of a specific employee).

The processed data is examined and analyzed in this phase to identify patterns, trends, and relevant information. Here, data goes through the following stages:

Speaker identification: The tool identifies speakers (customer, agent) to attribute specific statements or sentiments to individuals.

Keyword extraction: Relevant keywords and phrases are extracted to identify common themes or issues discussed during the calls.

Categorization: Calls are categorized based on predefined criteria, such as product issues, customer complaints, or positive feedback.

3. Data analysis

This phase involves deriving meaningful insights and actionable information from the analyzed data.

Pattern recognition: Speech analytics tools identify recurring patterns or trends in customer-agent interactions, helping understand common issues or concerns.

Performance metrics: Insights into agent performance, customer satisfaction levels, and areas for improvement are generated.

Root cause analysis: The tools can help pinpoint the root causes of common issues or challenges by analyzing the content and context of conversations.

Report generation: The insights are compiled into reports and dashboards, providing a comprehensive overview of call trends, customer sentiments, and operational performance.

4. Insight generation

Speech analytics is a component of conversation intelligence, focusing on the call recording and transcription aspects that convert interactions into business outcomes.

Within the broader scope of conversation intelligence, various elements are encompassed, covering end-to-end operations, such as:

Is speech analytics the same as conversation intelligence?

This means regularly checking how customer calls are handled to ensure they meet set service standards. Automated QA uses technology to make this process smoother, using tools to analyze interactions and check if they follow the rules.

Quality monitoring and automated QA

This includes organized methods for coaching and training agents. Workflows are about planning and carrying out activities to boost agent performance, improve skills, and fix any issues found by looking at how agents interact with customers.

Coaching and training workflows

Performance analytics means tracking, analyzing, and understanding essential indicators (like response times and customer satisfaction scores) that show how well a contact center is doing. These metrics give insights into how efficiently agents are operating overall.

Performance analytics

Real-time AI means using artificial intelligence that works instantly, providing immediate analysis and decision-making during live interactions. This tool offers quick insights, allowing for rapid adjustments and responses.

Real-time AI

Generative AI uses artificial intelligence systems that independently create content or responses. In conversation intelligence, generative AI can generate natural language responses, suggestions, or other content to assist agents and improve the overall quality of customer interactions.

Generative AI

Speech and text analytics are branches of natural language processing (NLP) that involve the analysis of spoken or written language, respectively, to extract meaningful insights and information.

Speech and text analytics: let's delve into the basics

Let’s consider how they differ:

Speech analytics

Text analytics

Data source

Spoken language, typically in real time, is recorded from phone conversations or other audio sources.

Written language can include various sources such as emails, social media posts, articles, etc.

Channels optimized for

Two-sided exchanges such as service, support, and sales calls.

One-side feedback such as surveys, customer reviews, social media, etc.


More complex, as it involves addressing challenges related to different dialects, accents, background noise, and other aspects associated with audio information.

Can be more straightforward since text is usually more structured and less susceptible to external factors such as intonation and pronunciation.


Often used in audio transcription, analysis of emotional tone in speech, and other areas. It helps organizations understand customer sentiment, identify key topics, and assess agent performance during phone interactions.

Applied for social media monitoring, document processing, automated responses to questions, and various other applications.

So, what is real-time speech analytics? In simple terms, speech analytics converts phone calls into text and then works with the text rather than the audio. This is because text fragments are easier to segment, sort by keywords, and identify objections, threats, or other expressions.

Further analysis allows businesses to promptly identify and resolve problematic issues, enhancing the efficiency of operator work. Recently, the quality of call transcription is not just acceptable but very high: with good recording quality and the absence of extraneous noises, accuracy reaches 99%.

Let Our Journey Begin!

This is a big deal when it comes, for example, to the sales department, where employees make at least 100 calls daily with an average duration of 5 minutes, that's already 500 minutes. Manually processing such volumes is time-consuming and expensive.

Transcribing calls into text and further analyzing textual information comes in handy. The obtained text is much easier to process than most audio files.

Contact center data can offer a more profound insight into customer experiences and business outcomes, but this is achievable only through proper management. Currently, many companies track only about 5% of calls, leading to siloed and inaccessible data. In contrast, speech analytics tools can transcribe 100% of conversations using AI. This technology accurately recognizes speech in audio recordings, translates it into text, and generates comprehensive reports on the conversations.

However, in addition to its primary purpose, speech analytics helps telecom companies diversify their income, strengthen their position in the market, increase the value of their services, and attract and retain customers.

But if you represent a telecom company, don't rush to launch a project on developing such a tool for your business telecommunication system immediately. In this article, we will describe in detail what is under the hood of this technology and answer why developing speech analytics tools from scratch means throwing money away.

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