Picture a call center overwhelmed with a surge of customer inquiries. Agents are struggling to keep up, leading to long wait times and frustrated customers. This scenario is all too common, and it can have severe consequences for a company's reputation and bottom line.

Call centers are turning to text-to-speech (TTS) technology to address these challenges. TTS can automate routine inquiries (e.g., providing account info, FAQs), streamline operations, provide 24/7 support, and improve the customer service experience.

In this article, you will learn:

• The various applications and benefits of TTS in call centers
• The challenges that call centers face when adopting this technology
• How to implement TTS in a call center
• Best practices for designing effective TTS applications for call centers

Call centers can make informed decisions and use TTS to provide excellent customer service while optimizing resources by understanding its potential and addressing its challenges. 

Text-to-speech (TTS) technology transforms written text into spoken words, mimicking the natural flow and intonation of human speech. TTS has various applications, such as enabling hands-free access to information while driving or assisting visually impaired individuals in consuming written content. 

Text-to-speech use cases can be classified into two categories: high production (major focus on superb voice quality) and high throughput. High throughput use cases are API-driven and all about scale and speed—handling many short, real-time conversations between humans and AI agents.

In call centers, TTS is crucial for many applications:

• Interactive Voice Response (IVR): Guiding callers through menus and providing automated responses.
• Customer Notifications: Delivering personalized messages about account updates, order confirmations, or appointment reminders.
• Agent Assist: Providing real-time support to agents during calls, reading out relevant information, or suggesting responses.

Modern TTS systems—Neural Text-to-Speech (NTTS)—use advanced algorithms, including deep learning, natural language processing (NLP), and generative models, to generate remarkably human-like voices on the fly. 

These algorithms are trained on massive datasets of human speech that allow them to learn the nuances of pronunciation, intonation, and rhythm.

The typical modern TTS architecture comprises four key components:

• Pre-processor: This component prepares the input text for synthesis. It performs tasks such as text normalization (e.g., expanding abbreviations, converting numbers to words), tokenization (splitting text into words or sub-word units), and linguistic analysis to predict prosody (intonation and stress patterns).
• Encoder: The encoder transforms the pre-processed text into a high-dimensional numerical representation (vectors), often using techniques like recurrent neural networks (RNNs) or transformers. This representation captures the linguistic features of the text, such as word meanings, sentence structure, and contextual information.
• Decoder: The decoder generates an acoustic representation of the speech, usually in the form of mel spectrograms. Mel spectrograms capture the spectral characteristics of sound over time, representing how different frequencies contribute to the overall sound.

Vocoder: Finally, the vocoder takes the mel spectrograms and synthesizes the actual audio waveform, which can be played back as audible speech. Modern vocoders often use deep learning models like WaveNet or neural vocoders to achieve high-quality, natural-sounding speech.

The story you read at the beginning of this article gives a sneak peek at why TTS is crucial in customer service operations. As it is commonly said: Customers are the heart of your business. 

Customers face challenges when using your products and need prompt resolution. Delays in resolving these issues result in customer dissatisfaction, which affects a business's success and reputation. Text-to-speech technology offers a multitude of benefits that, overall, aim to improve efficiency and customer satisfaction.

This section explains the benefits of TTS in call centers.

TTS technology enables call centers to automate routine interactions, such as answering frequently asked questions, providing account information, and guiding customers through simple processes. 

This automation leads to faster service, as customers receive immediate responses without waiting for a live agent.

Call centers can efficiently handle a large volume of incoming calls using TTS-powered interactive voice response (IVR) systems. Customers can self-serve through automated menus and access information without waiting in lengthy queues. This results in significantly reduced wait times and an improved customer experience.

With TTS managing routine tasks, live agents can dedicate their expertise to addressing more complex customer issues that require human intervention. This allows agents to provide personalized support, troubleshoot intricate problems, and deliver a higher level of service.

This also enhances job satisfaction for agents, who can focus on more engaging and challenging work.

TTS systems operate around the clock, ensuring that customers have access to information and support at any time of day or night. This 24/7 availability is crucial for global businesses and improves customer satisfaction by providing consistent, reliable service regardless of time zones.

TTS technology can be easily configured to support multiple voice accents, genders, and languages, allowing call centers to cater to a global customer base. This eliminates language barriers, improves accessibility for non-native speakers, and enhances the customer experience for diverse populations.

TTS can access customer data and tailor interactions accordingly by integrating with customer relationship management (CRM) systems. This personalization, such as addressing customers by name or referencing their previous interactions, tailors responses based on their specific needs and preferences.

This level of personalization creates a more engaging and satisfying experience between the customer and the brand.

Compared to employing a large team of live agents, implementing TTS technology is a cost-effective solution. It reduces the need for extensive staffing, lowers operational expenses, and provides a scalable approach to handling call volumes, especially during peak hours.

Overall, businesses can create a more streamlined, responsive, and personalized call center experience for their customers. In the following section, you will learn how to implement TTS for call center applications.

Implementing text-to-speech (TTS) technology in a call center involves a systematic approach that encompasses careful planning, integration with existing systems, and ongoing optimization. 

Here's a step-by-step guide to help you navigate the process:

Clearly outline your goals for implementing TTS. Are you looking to automate routine tasks, reduce wait times, personalize interactions, or achieve cost savings? Defining your objectives will guide your technology choices and implementation strategy.

Evaluate your call center's specific requirements. Clearly define the TTS requirements, including:

• Specific use cases for TTS (e.g., IVR menus, self-service options, customer notifications)
• Language and voice preferences to cater to your customer base
• Integration needs with existing infrastructure (call center systems and CRM platforms)
• Performance and scalability expectations (based on call volume)
• Hosting options
• Budget constraints

Inevitably, your use case would fall into either of the categories you learned earlier—high production or throughput. The assessment will help you determine the most suitable TTS solution for your needs.

Research and compare different TTS solution providers, such as Deepgram's Aura, to find the best fit for your call center.

Deepgram's Aura Text-to-Speech API offers high voice quality, customization, and integration with various call center platforms. It is the first text-to-speech model built for responsive, conversational AI agents and applications.

Aura uses a state-of-the-art deep learning architecture based on Transformer models that capture intricate patterns in language and speech. 

Unlike traditional concatenative TTS systems that rely on piecing together pre-recorded speech fragments, Aura's neural network generates speech waveforms directly from text input. This results in more natural-sounding voices with improved prosody. 

The corresponding speech forms natural cadences that include pauses, audible breaths, and hesitation sounds like 'uh' and 'um,' as well as dynamic adjustments in tone and emotion to suit the conversation's context.

Deepgram Aura TTS features:

• Fast, Low-Latency Voices: Aura voice outputs have exceptionally low latency. In our performance tests, Aura consistently delivered voice outputs with an average response time below 250 milliseconds for typical dialogue sequences. This is 2-5 times faster than leading competitors for seamless interactions between users and AI agents.
• Scale and Efficiency: Aura's efficient processing capabilities also enable it to handle high-throughput scenarios with ease. It can generate voice outputs for thousands of concurrent requests without any noticeable degradation in performance or quality. Deepgram also has concurrency rate-limits implementation for better control of API usage. This scalability makes Aura an ideal choice for businesses with large-scale conversational AI deployments.
• Customization for Developers: Developers can use Aura's API to prompt voices to match specific brand identities or customer demographics. This includes adjusting parameters such as pitch, speed, and emphasis, as well as selecting from a wide range of pre-trained voices or creating entirely new custom voices.
• Cost Effective for High-Throughput Applications: Aura uses a usage-based pricing scheme and starts at just $0.015/1K characters. It scales to more than 13M characters' worth of voice synthesis using any of the available voices.
• Flexible Hosting Options: Developers can access Deepgram through the managed platform or with a self-hosted offering that can be used with virtual private cloud providers, such as AWS, GCP, Oracle, or Azure, as well as with bare-metal deployments.
• Extensive Media Output Settings: Deepgram provides support for generating audio output in various formats, each with specific encoding options.

Deepgram provides SDK libraries with popular programming languages such as Python, JavaScript, and Go, making it easy for developers to integrate the Aura API into their projects.

Aura also provides comprehensive documentation, code samples, and SDKs to help with the implementation process. Developers can quickly get started with Aura and use its features to improve their conversational AI applications.

You can test Aura's performance, experiment with different voices, and evaluate its suitability for your specific use cases on Deepgram’s API playground.

If you prefer hacking it out, here’s a code example in Python to generate a voice output after installing the Deepgram SDK and obtaining your API key to try out yourself:

from deepgram import Deepgram, DeepgramClient, SpeakOptions

# Your Deepgram API Key
DEEPGRAM_API_KEY = "<your-api-key>"

# Initialize the Deepgram SDK

SPEAK_OPTIONS = {"text": "Welcome to our customer service center. How can we assist you today?"}
filename = "voice_output.mp3"

def main():
    try:
        # STEP 1: Create a Deepgram client.
        # By default, the DEEPGRAM_API_KEY environment variable will be used for the API Key
        deepgram = DeepgramClient(DEEPGRAM_API_KEY)

        # STEP 2: Configure the options (such as model choice, audio configuration, etc.)
        options = SpeakOptions(
            model="aura-asteria-en",
        )

        # STEP 3: Call the save method on the speak property
        response = deepgram.speak.rest.v("1").save(filename, SPEAK_OPTIONS, options)
        print(response.to_json(indent=4))

    except Exception as e:
        print(f"Exception: {e}")

if __name__ == "__main__":
    main()

You should get a voice output similar to the one below:

That sounds... uhm... human to me, eh? 😉 Sweet! We encourage you to join our developer community forum to ask questions, share ideas, and discover how Aura can elevate your customer interactions.

Work with your chosen TTS solution provider to integrate the technology with your existing call center systems, such as:

• IVR platforms
• CRM software
• Workforce management tools
• Analytics and reporting systems
• Agent desktops

Create a detailed integration plan that outlines the data flows, API integrations, and any necessary customizations to map compatibility between the TTS solution and your current infrastructure.

In addition to the SDK libraries you can use to access Deepgram APIs, it also supports a wide range of integrations to call center platforms (e.g., Amazon Connect and Genesys), analytics tools, automation tools (e.g., Zapier), and agentic tools (e.g., Dialogflow CX).

Daily, for instance, is a real-time voice, video, and AI platform for developers. They successfully added Deepgram Aura to their production stack for LLM-powered voice use cases. 

Collaborate with your team to design and develop TTS applications tailored to your call center's needs. This process involves:

• Creating clear and concise TTS prompts and menus
• Personalizing TTS interactions using customer data
• Implementing self-service options (from FAQs, existing knowledge bases, automated responses, chatbots, etc.) to reduce live agent workload
• Conducting thorough testing and quality assurance

Ensure the test results align with your brand voice and cater to the needs of your target audience.

Provide comprehensive training to your staff on how to effectively use and manage the TTS system in a controlled environment before deploying them to your live call center. Cover topics like:

• Navigating the TTS interface and controls
• Handling customer inquiries and escalations related to TTS interactions
• Monitoring and reporting on TTS performance
• Best practices for maintaining and optimizing the TTS system

Gather feedback from users and refine the application and system configurations to optimize performance and ensure a seamless customer experience.

Once your TTS system is fully integrated and tested, launch it in your call center. Closely monitor its performance and gather feedback from both customers and staff. Key metrics to track include:

• Customer satisfaction scores;
• Call resolution times;
• Self-service success rates;
• System uptime and reliability; to identify areas for improvement.

Another implementation is Humach’s use of Deepgram’s Aura Text-to-Speech (TTS) into its conversational flows to help retail clients manage the ordering process. 

"We saw a big improvement in the accuracy and speed of transcription when we switched from a cloud vendor's service to Deepgram's Nova-2. With Aura's text-to-speech, we can now reach speeds that are 2–5 times faster than our competitors. 

At the same time, we can maintain the voice quality and latency that are needed for low handle times and first call resolutions. Deepgram’s infrastructure serves high-quality, reliable models that support  our seasonal traffic for retail, utility, travel, and healthcare use cases."

–Tim Houlne, CEO at Humach

Here’s another application of Aura in the wild from the developers at Vapi.ai with a personalized experience for the customer:

Regularly analyze TTS performance data and customer feedback to identify areas for improvement. 

Work with your TTS solution provider to:

• Fine-tune TTS prompts and menus for better clarity and efficiency
• Update TTS models to use advancements in speech technology (e.g., at Deepgram, our researchers work around the clock to continually improve our models)
• Optimize system performance and scalability as call volumes grow
• Expand TTS capabilities to support new languages or use cases

Following this structured approach and partnering with a reliable TTS solution provider like Deepgram, your call center can successfully implement TTS technology and start reaping its benefits for customers and business.

At Deepgram, we are at the forefront of seeing how TTS is evolving and becoming increasingly useful for our customers building call centers and customer support systems.  As technology continues to advance and customer expectations evolve, we anticipate several exciting developments. 

Here are five key trends for the future of TTS in this industry:

1. Hyper-Personalization: With RAGs and long-context window LLMs getting better, TTS applications will use real-time customer data and behavioral insights to deliver hyper-personalized experiences. Imagine a scenario where a TTS system not only addresses a customer by name but also tailors its responses based on the customer's purchase history, browsing behavior, and emotional state.
2. Multilingual and Cross-Cultural Communication: While this is already happening, TTS technology will seamlessly bridge language barriers and cultural nuances, including low-resource languages. This will enable call centers to cater to a global audience with culturally sensitive and linguistically accurate interactions.
3. Emotionally Intelligent Interactions: TTS systems will be equipped with emotional intelligence capabilities, allowing them to detect and respond to customers' emotions in real time. This will enable call centers to deliver empathetic and supportive interactions that can improve customer satisfaction and loyalty.
4. Proactive Customer Engagement: TTS applications will evolve from reactive tools to proactive ones, especially with the continuous rise of AI agents. This proactivity will include reaching out to customers with personalized offers, reminders, and updates. This will foster stronger relationships with customers and drive engagement.
5. Integration with Omnichannel Communication: As customers increasingly interact with businesses through various channels (e.g., phone, chat, email, social media), TTS will be used to maintain a consistent voice and tone across all platforms. This integration will help create a cohesive and unified customer experience, regardless of the communication channel.

We are already implementing most of these trends in our TTS and STT (speech-to-text) APIs, so our customers can stay ahead of their competitors and deliver exceptional service in an increasingly competitive landscape.

Throughout this article, we have explored the numerous benefits of implementing TTS in call centers, including increased automation, reduced wait times, improved agent productivity, 24/7 availability, multilingual support, personalized interactions, and cost savings. 

We have also discussed the key considerations for selecting a TTS solution, such as natural-sounding speech, language support, integration capabilities, and customization options.

Deepgram's Aura offers advanced AI-powered speech synthesis, integration with existing systems, and a range of features designed to meet the unique needs of call centers. With Aura, we have seen implementations from partners that have elevated their customer service and optimized their operations to improve customer satisfaction.

You can position your call center for digital success by following the best practices in this article and staying current on TTS technology. Speaking of staying current, check out Deepgram’s resource channel, which includes podcast conversations, webinars, whitepapers, articles, and the latest resources on voice AI.

Text-to-speech is a technology that converts written texts into natural-sounding human-like speech.

There are so many benefits to TTS models in call centers. Cost reduction, improved customer experience, scalability, and personalized interactions are some benefits of text-to-speech models in call centers.

When designing a TTS model, it is crucial to consider factors such as latency, data security, data privacy, and voice quality. Deepgram Aura excels in all these areas while being cost-effective.