Month: May 2023

Large Language Models (LLMs) and AI applications such as ChatGPT and DALL-E have recently seen rapid growth. Thanks to GPUs, CPUs, DPUs, high-speed storage, and…

Large Language Models (LLMs) and AI applications such as ChatGPT and DALL-E have recently seen rapid growth. Thanks to GPUs, CPUs, DPUs, high-speed storage, and AI-optimized software innovations, AI is now widely accessible. You can even deploy AI in the cloud or on-premises.

Yet AI applications can be very taxing on the network, and this growth is burdening CPU and GPU servers, as well as the existing underlying network infrastructure that connects these systems together.  

Traditional Ethernet, while sufficient for handling mainstream and enterprise applications such as web and video or audio streaming, is not optimized to support the new generation of AI workloads. Traditional Ethernet is ideal for loosely coupled applications, low-bandwidth flows, and high jitter. It might be sufficient for heterogeneous traffic (such as web, video, or audio streaming; file transfers; and gaming) but is not ideal when oversubscription occurs. 

Designed from the ground up to meet the performance demands for AI applications, NVIDIA Spectrum-X networking platform is an end-to-end solution that is optimized for high-speed network performance, low latency, and scale.

NVIDIA Spectrum-X

NVIDIA Spectrum-X networking platform was developed to address traditional Ethernet network limitations. It is a network fabric designed to answer the needs of demanding AI applications, intended for tightly coupled processes. 

This NVIDIA-certified and tested end-to-end solution combines the best-in-class, AI-optimized networking hardware and software to provide a predictable, consistent, and uncompromising level of performance required by AI workloads. 

NVIDIA Spectrum-X is a highly versatile technology that can be used with various AI applications. Specifically, it can significantly enhance the performance and efficiency of AI clusters in the following use cases: 

• GPT and BERT LLMs 
• Distributed training and parallel processing 
• Natural language processing (NLP)
• Computer vision  
• High-performance simulation (NVIDIA Omniverse and NVIDIA OVX) 
• High-performance data analytics (Spark) 
• Inference applications 

The two key elements of the NVIDIA Spectrum-X platform are the NVIDIA Spectrum-4 Ethernet switch and the NVIDIA BlueField-3 DPU. 

NVIDIA Spectrum-4 Ethernet switch

NVIDIA Spectrum-4 Ethernet switch provides unprecedented application performance for AI clusters built on standards-based Ethernet. Realizing the full potential of NVIDIA Spectrum-4 requires an end-to-end, purpose-built network architecture. Only the NVIDIA Spectrum-X platform provides the hardware accelerators and offloads needed to power hyperscale AI. 

NVIDIA Spectrum-4 Ethernet switches are built on the 51.2 Tbps Spectrum-4 ASIC, with 4x the bandwidth of the previous generation. It is the world’s first Ethernet AI switching platform. It was designed for AI workloads, combining specialized high-performance architecture with standard Ethernet connectivity. 

NVIDIA Spectrum-4 offers:

• RoCE extensions: RoCE with unique enhancements
  • RoCE Adaptive Routing
  • RoCE Performance Isolation
  • Simplified, Automated Adaptive Routing and RoCE Configurations
  • Synchronized Collectives
  • Other RoCE for HPC enhancements
• Highest effective bandwidth on Ethernet at scale
• Low latency with low jitter and short tail 
• Deterministic performance and performance isolation
• Full stack and end-to-end optimization
• NVIDIA Cumulus Linux or SONiC

Key benefits of NVIDIA Spectrum-X with NVIDIA Spectrum-4 include the following:

• Using RoCE extension for AI and adaptive routing (AR) to achieve maximum NVIDIA Collective Communication Library (NCCL) performance.
• Leveraging performance isolation to ensure that in a multi-tenant and multi-job environment, one job does not impact the other.
• Ensuring that if there is a network component failure, the fabric continues to deliver the highest performance
• Synchronizing with BlueField-3 DPU to achieve optimal NCCL and AI performance
• Maintaining consistent and steady performance under various AI workloads, vital for achieving SLAs.

End-to-end optimal network performance

To build an effective AI compute fabric requires optimizing every part of the AI network, from DPUs to switches to networking software. Achieving the highest effective bandwidth at load and at scale demands using techniques such as RoCE adaptive routing and advanced congestion control mechanisms. Incorporating capabilities that work synchronously on NVIDIA BlueField-3 DPUs and Spectrum-4 switches is crucial to achieve the highest performance and reliability from the AI fabric. 

RoCE adaptive routing

AI workloads and applications are characterized by a small number of elephant flows responsible for the large data movement between GPUs, where the tail latency highly impacts the overall application performance. Catering to such traffic patterns with traditional network routing mechanisms can lead to inconsistent and underutilized GPU performance for AI workloads.

RoCE adaptive routing is a fine-grained load balancing technology. It dynamically reroutes RDMA data to avoid congestion and provide optimal load balancing to achieve the highest effective data bandwidth. 

It is an end-to-end capability that includes Spectrum-4 switches and BlueField-3 DPUs. The Spectrum-4 switches are responsible for selecting the least-congested port for data transmission on a per-packet basis.  As different packets of the same flow travel through different paths of the network, they may arrive out of order to their destination. The BlueField-3 transforms any out-of-order data at the RoCE transport layer, transparently delivering in-order data to the application. 

Spectrum-4 evaluates congestion based on egress queue loads, ensuring all ports are well-balanced. For every network packet, the switch selects the port with the minimal load over its egress queue. Spectrum-4 also receives status notifications from neighboring switches, which influence the routing decision. The queues evaluated are matched with the quality-of-service level. 

As a result, NVIDIA Spectrum-X enables up to 95% effective bandwidth across the hyperscale system at load, and at scale. 

RoCE congestion control

Applications running concurrently on hyperscale cloud systems may suffer from degraded performance and reproducible run-times due to network level congestion. This can be caused by the network traffic of the application itself, or background network traffic from other applications. The primary reason for this congestion is known as many-to-one congestion, where there are multiple data senders and a single data receiver.

Such congestion cannot be solved using adaptive routing and actually requires data-flow metering per endpoint. Congestion control is an end-to-end technology, where Spectrum-4 switches provide network telemetry information representing real time congestion data.  This telemetry information is processed by the BlueField DPUs, which manage and control the data sender’s data injection rate, resulting in maximum efficiency of network sharing.  

Without congestion control, many-to-one scenarios will cause network back-pressure and congestion spreading or even packet-drop, which dramatically degrade network and application performance.

In the congestion control process, BlueField-3 DPUs execute the congestion control algorithm. They handle millions of congestion control events per second in microsecond reaction latency and apply fine-grained rate decisions. 

The Spectrum-4 switch in-band telemetry holds both queuing information for accurate congestion estimation, as well as port utilization indication for fast recovery. NVIDIA RoCE congestion control significantly improves congestion discovery and reaction time by enabling the telemetry data to bypass the congested flow queueing delay while still providing accurate and concurrent telemetry.

RoCE performance isolation 

AI hyperscale and cloud infrastructures need to support a growing number of users (tenants) and parallel applications or workflows. These users and applications inadvertently compete on the infrastructure’s shared resources, such as the network, and therefore may impact performance. 

The NVIDIA Spectrum-X platform includes mechanisms that, when combined, deliver performance isolation.  It ensures that one workload cannot impact the performance of another. These mechanisms ensure that any workload cannot create network congestion that will impact data movement of another workload. The performance isolation mechanisms include quality of service isolation, RoCE adaptive routing for data path spreading, and RoCE congestion control. 

The NVIDIA Spectrum-X platform features tight integration of software and hardware, enabling deeper understanding of AI workloads and traffic patterns. Such an infrastructure provides the capabilities to test with large workloads using a dedicated Ethernet AI cluster. By leveraging telemetry from Spectrum Ethernet switches and BlueField-3 DPUs, NVIDIA NetQ can detect network issues proactively and troubleshoot network issues faster for optimal use of network capacity. 

The NVIDIA NetQ network validation and ASIC monitoring tool set provide visibility into the network health and behavior. The NetQ flow telemetry analysis shows the paths that data flows take as they traverse the network, providing network latency and performance insights.

Increased energy efficiency

Power capping has become a common practice in data centers due to the growing demand for computing resources and the need to control energy costs. The Spectrum-4 ASIC and optical innovations enable simplified network designs that improve performance per watt, achieving better efficiency and delivering faster AI insights, without exceeding network power budgets. 

Summary

NVIDIA Spectrum-X networking platform is designed especially for demanding AI applications. With higher performance compared to traditional Ethernet, lower power consumption, lower TCO, full stack software-hardware integration, and massive scale, NVIDIA Spectrum-X is the ideal platform for running existing and future AI workloads. 

Learn more

Looking for more information? Check out these resources:

• NVIDIA Spectrum-X Network Platform Architecture whitepaper
• NVIDIA Spectrum-X demo video
• NVIDIA Spectrum-X site
• NVIDIA Spectrum-4 datasheet
• NVIDIA Spectrum SN5000 Series Switches datasheet
• NVIDIA Cumulus Linux
• NVIDIA NetQ
• NVIDIA Collective Communication Library (NCCL)

The need for a high-fidelity multi-robot simulation environment is growing rapidly as more and more autonomous robots are being deployed in real-world…

The need for a high-fidelity multi-robot simulation environment is growing rapidly as more and more autonomous robots are being deployed in real-world scenarios. In this post, I will review what we used in the past at Cogniteam for simulating multiple robots, our current progress with NVIDIA Isaac Sim, and how Nimbus can speed up the development and maintenance of a multi-robot simulation with Isaac Sim.   

Multi-robot simulation with Unreal Tournament game engine 

About 20 years ago, my friends at Cogniteam and I started our robotic development careers with the idea of a robotic framework for multi-robot task allocation and teamwork. Originally called CogniTAO, a simplified version of this system was later published as ROS decision_making. 

At the time, use cases for multiple robots were scarce, and 3D simulation for those robots was not possible. So I wrote a mod for the Unreal Tournament 2000-2004 game engine to enable simulation for four robots. It took our small team of four programmers about 3 years to develop a simulated environment that could reliably run for 15 minutes.

Figure 1. Simulation of four robots (left) and video from the robots (right)

This environment was able to simulate four robots with a camera, Hokuyo LiDAR, odometry, and mapping on five state-of-the-art desktops, and remotely receive video feeds from each. One of our engineers wrote a C++ TCP client that would stream the data on the local network directly from the game engine and display it in fullscreen. We had to run the code in strict order to make the robots spawn on time and in the correct place.

Multi-robot simulation with Gazebo

Fast forward 10 years to 2013 when we transitioned our work to Gazebo after it became the de facto platform for robotic simulation. It took three programmers about 2 years to simulate 10 robots on two Intel Xeon machines. They used the ROS move_base navigation stack and object detection using OpenCV Hough Circle Transform—what robotics teams used for demos before TensorFlow. Igor Makhtes, our colleague at the time, built the RQT plugin to control and show data streams from multiple robots (Figure 2). It took him 6 months to complete.

These robots had to communicate with each other, but also needed to operate when a connection was unavailable. To make this possible, each had to run its own ROS master and sync through a ROS multimaster network.

Multi-robot simulation with NVIDIA Isaac Sim

A few months ago, I asked Saar Moseri, a computer science student on our algorithmic team at Cogniteam to set up a multi-robot simulative scenario using the cloud robotics ecosystem Nimbus and NVIDIA Isaac Sim. Our internal test team and I hoped to use the Nimbus agent to control our robots and view the data they generate. 

It took Saar about 2 weeks to familiarize himself with the environment and configure the system. Figure 3 shows the result of this effort, running on a standard (single) desktop machine with an NVIDIA GeForce RTX 3080 in the Cogniteam lab.

Saar used the Isaac Sim documentation available through NVIDIA NGC to install and set up the environment. Using Nimbus, he installed an agent on the simulation machine and created a gateway node to receive data from the simulation through ROS.

 Figure 4. Nimbus robot editor (left) and Nimbus configuration editor (right)

We then created the node configuration shown in Figure 5.

The two building blocks (already containerized) are a gateway node and a node for move_base navigation. This configuration was deployed to the agent running on the simulation desktop in the Cogniteam lab. Other more complex configurations are also available (with sources) in the Nimbus hub, including nodes for GMapping, path following, and more. 

My team and I were stunned by the endless possibilities this approach enables. In the configuration described above, simulated sensory data arrives from Isaac Sim through the ROS gateway, which supports both ROS and ROS 2. View and control capabilities are enabled by Nimbus. 

Out of the box, this setup enables our team to carry out basic simulation tasks and simulate the control of a robot fleet locally in our lab, along with many more capabilities. We can now record simulated runs and sensory data from robots, remotely SSH into a simulation machine, monitor simulation data globally, and even send email and SMS notifications about simulation progress to our validation team—all from a web browser. 

Combining Isaac Sim with Nimbus results in a unified system that is similar in features to available cloud simulation offerings, but runs on a local machine and does not involve additional cloud simulation compute costs. Additionally, it opens new cutting-edge simulation flows, such as simulation with hardware in the loop. This is not possible when the simulation runs in the cloud. Figure 6 shows how the control, navigation, and mapping look in Nimbus.

Figure 6. Nimbus robot WebRTC video monitoring (left) and Nimbus map view and autonomy control (right)

To replicate the setup described, reference the Isaac Sim documentation. Then visit Nimbus to create a free account, log in, and follow the instructions to create a robot using a free license. 

After the robot agent is installed on the same desktop that Isaac Sim is running headless, you will be able to provision the simulation through remote SSH and monitor the simulation machine from the Nimbus website.

Visit the Nimbus hub to deploy the Isaac Sim configuration. Since everything is already containerized (including Isaac Sim) and control is browser-based, you do not need to install any applications. The agent on the machine will set up everything needed to execute. 

Then, on the monitor page of that agent, add monitoring for any data that is relevant to your setup. In the agent settings, you can define notifications by adding conditions on ROS streams such as:

“if GoalStatus == ABORTED”
send sms/mail to [email protected]

Cogniteam is happy to help you in the process. You can reach us at [email protected]. 

Summary

For the successful deployment of autonomous robots, simulation is key. Running the same scenario multiple times is crucial for testing, but multi-robot simulations differ. Developing a high-fidelity multi-robot simulated environment is complex and takes time, but it can be simplified with NVIDIA Isaac Sim and Nimbus, as described in this post.

My team and I will be attending ICRA 2023 in London, May 29 to June 2 (Booth C22), showcasing our browser interface to robots and simulations running remotely in Israel.

To learn more about Isaac Sim, check out the NVIDIA Developer Isaac ROS Forum.

The telecom sector is transforming how communication happens. Striving to provide reliable, uninterrupted service, businesses are tackling the challenge of…

The telecom sector is transforming how communication happens. Striving to provide reliable, uninterrupted service, businesses are tackling the challenge of delivering an optimal customer experience.

This optimal customer experience is something many long-time customers of large telecom service providers do not have. Take Jack, for example. His call was on hold for 10 minutes, which made him late for work. Jill, the third agent he spoke with, read the brief note provided by the previous agent but had trouble understanding it. So, she asked Jack a few questions to clarify. With no co-workers available, Jill consulted multiple policy documents to address Jack’s concerns. Several resources later, Jill located the necessary information, but sadly, Jack had already ended the call.

Long wait times, complex service requests, and a lack of personalization are some of the common issues faced by customers, leading to dissatisfaction and churn. To overcome these challenges, the telecom sector is turning to AI—specifically conversational AI, a technology that leverages speech, translation, and natural language processing (NLP) to facilitate human-like interactions.

This post explores why conversational AI systems are essential and why it is important to have a high level of transcription accuracy for optimal performance in downstream tasks. We explain the NVIDIA Riva speech recognition customization techniques Quantiphi has used to improve transcription accuracy.

Join us on June 7 for the webinar Empower Telco Contact Center Agents with Multi-Language Speech-AI-Customized Agent Assists featuring live demos from Infosys, Quantiphi, and NVIDIA.

Accuracy in conversational AI systems

In telco contact centers, highly accurate conversational AI systems are essential for several reasons. Conversational AI systems can help agents extract valuable information from call interactions and make informed decisions, leading to improved service quality and customer experience. 

One key component in a conversational AI system is automatic speech recognition (ASR), also known as speech recognition or speech-to-text. Downstream tasks in telco contact centers heavily rely on accurate transcription provided by ASR systems. These tasks encompass a wide range of applications such as: 

• Customer insights
• Sentiment analysis
• Call classification
• Call transcription

Quick and accurate responses are vital for efficient and effective customer service. That means reducing the overall latency of individual components, including ASR, is very important. By reducing the time required to complete a task, contact center agents can provide prompt solutions, leading to enhanced customer satisfaction and loyalty.

Moreover, accurate transcription that includes punctuation enhances readability. Clear and well-punctuated transcriptions help agents better understand customer queries, facilitating clear communication and problem solving. This, in turn, improves the overall efficiency and effectiveness of customer interactions.

NVIDIA Riva automatic speech recognition pipeline

Speech-to-text receives an audio stream as input, transcribes it, and produces the transcribed text as output (Figure 1). First, the audio stream goes to an audio feature extractor and preprocessor, which filter out noise and capture audio spectral features in a spectrogram or mel spectrogram. Then, an acoustic model, together with a language model, transcribes the speech into text. Punctuation is added to the transcribed text to improve readability. 

Performance evaluation metrics for ASR systems

The performance of an ASR system can be measured using three metrics:

1. Accuracy is fundamental, as it directly affects the quality and reliability of the transcriptions. By measuring accuracy through metrics like word error rate (WER), the system can be evaluated in terms of how well it transcribes spoken words. A low WER is vital in contact centers, as it ensures that customer queries and interactions are precisely captured, enabling agents to provide accurate and appropriate responses.
2. Latency is the time taken to generate a transcript of a segment of audio. To maintain an engaging experience, the caption should be delivered at a latency of no more than a few hundred milliseconds. A transcription system must deliver captions with minimal delay. Low latency ensures a seamless and engaging customer experience, enhancing overall efficiency and customer satisfaction.
3. Cost to develop and run a transcription service on sufficient compute infrastructure is another important measure. Although AI-based transcription is inexpensive compared to human interpreters, cost must be weighed along with other factors.

In a contact center setting, a transcription system must excel in accuracy to provide reliable transcriptions, offer low latency for prompt customer interactions, and consider cost factors to ensure a cost-effective and feasible solution for the organization. By optimizing all three metrics, the transcription system can effectively support contact center operations and enhance delivery of customer service.

Methods to improve ASR accuracy 

As shown in Figure 2, there are several techniques that can be used to achieve the best possible transcription accuracy for a specific domain, the easiest of which is word boosting. ASR word boosting involves passing to the model a list of important, possibly out-of-vocabulary, domain-specific words as additional input. This enables the ASR module to recognize such words during inference.

In most cases, certain nouns (such as the names of companies or services) are either not in the vocabulary, or are frequently mistranscribed by the ASR model. These nouns were added to the list of words to be boosted. This strategy enabled us to easily improve recognition of specific words at request time.

In addition, the Quantiphi team:

• Retrained the language model on our own custom dataset to adapt the ASR engine to our domain-specific terms and phrases. 
• Fine-tuned the acoustic model to adapt the ASR engine to specific accents and noisy environments.

Customized speech-assisted conversational AI systems 

One of the most significant challenges faced by customer contact centers in the telecom industry is the long time it takes to resolve complex queries. Agents typically need to consult with multiple stakeholders and internal policy documents to respond to complex queries. 

Conversational AI systems provide relevant documentation, insights, and recommendations, thereby enabling contact center agents to expedite the resolution of customer queries. 

The Quantiphi solution architecture for customized speech-assisted conversational AI pipeline involves the following: 

1. Speech recognition pipeline: Creates transcriptions by capturing spoken language and converting it into text
2. Intent slot model: Identifies user intent 
3. Semantic search pipeline: Retrieves answers for the agent query through the dialog manager 

Quantiphi built a semantic search engine and a question-answering solution (Figure 3). It retrieves the most relevant documents for a given query and generates a concise answer for telco contact center agents.

ASR, in conjunction with question-answering (QnA) systems, is also used in virtual agents and avatar-based chatbots. The accuracy of ASR transcripts has a significant impact on the accuracy of agent assist, virtual agents, and avatar-based chatbots, since they are input to responses generated by a retrieval augmented generation (RAG) pipeline. Even a slight discrepancy in the way the query is transcribed can cause the generative model to provide incorrect responses. 

The Quantiphi team tried off-the-shelf ASR models, which sometimes failed to correctly transcribe proper nouns. The quality of the ASR transcription is of paramount importance when it is used in conjunction with question – answering pipelines, as shown in the following example:

Query: What is 5G?

ASR transcript: What is five g.

Generator response: Five grand is the amount of money you can earn if you work in a factory for a month.

Correct response: 5G is the next generation of wireless technology. It will be faster, more reliable, and more secure than 4G LTE.

To overcome such issues, we have used word-boosting, inverse text normalization, custom vocabulary, training language models, and fine-tuning acoustic models.

Word boosting

Words (or acronyms) such as mMTC and MEC were often transcribed incorrectly. We have addressed this with the help of word boosting. Consider the following example:

Before word boosting

Multi axis edge computing, also known as MEG is a type of network architecture that provides cloud computing capabilities and an It service environment at the edge of the network.

Mtc Fis a service area that offers low bandwidth connectivity with deep coverage.

After word boosting

Multi access edge computing also known as MEC is a type of network architecture that provides cloud computing capabilities and an IT service environment at the edge of the network.

mMTC is a service area that offers low bandwidth connectivity with deep coverage.

The before and after show how responses change, even if there is a slight difference in the way an n-gram is represented. Through inverse text normalization, the ASR model transcribes words such as ‘five g’ as ‘5G’, thus improving the QnA pipeline’s performance in the process.

Adding customized vocabulary to ASR

Most use cases typically have certain domain-specific words and jargon associated with them. To include these words in the ASR output, we added them to the vocabulary file and rebuilt the ASR model. For more details, see the tutorial How to Customize Riva ASR Vocabulary and Pronunciation with Lexicon Mapping.

Training n-gram language models

The contexts present in QnA tasks typically form a good source of text corpus to train an n-gram language model. A customized language model results in ASR outputs that are more receptive to sequences of words that commonly appear in the domain. We used an NVIDIA NeMo script to train a KenLM model and integrated it with the ASR model at build time.

Fine-tuning acoustic models

To further improve ASR performance, we fine-tuned an ASR acoustic model with 10-100 hours of small chunks (5-15 seconds) of audio data, with their corresponding ground-truth text. This helped the acoustic model to pick up regional accents. We used the Riva Jupyter notebook and NeMo for this fine-tuning. We further converted this checkpoint to Riva format using the nemo2riva tool and built it using the riva-build command.

Key takeaways

Question-answering and insights extraction make up conversational solutions that empower telecom customer service agents to provide personalized and efficient support. This improves customer satisfaction and reduces agent churn. To achieve highly accurate QnA and insights extraction solutions, it is necessary to provide high-accuracy transcriptions as an input to the rest of the pipeline. 

Quantiphi achieved the highest possible accuracy by customizing speech recognition models with NVIDIA Riva ASR word boosting, inverse text normalization, custom vocabulary, training language models and fine-tuning acoustic models. This was not possible with off-the-shelf solutions. 

What does that mean for Jack and Jill? Equipped with telco-customized speech-assisted conversational AI applications, Jill can quickly scan through the AI-generated summary of Jack’s previous conversations. Just as Jack finishes asking a question, her screen is already populated with the most relevant document to resolve Jack’s query. She swiftly conveys the information to Jack. He decides to answer the survey with positive feedback and still arrives at work on time. 

Get in touch with experts at Quantiphi to embark on a comprehensive exploration of how conversational AI can profoundly augment your organization’s customer experience. If you are interested in diving deeper into the technical aspects of constructing agent assist solutions,  join us for the webinar, Empower Telco Contact Center Agents with Multi-Language Speech-AI-Customized Agent Assists.

According to Gartner,® “Nearly half of digital workers struggle to find the data they need to do their jobs, and close to one-third have made a wrong business…

According to Gartner,^® “Nearly half of digital workers struggle to find the data they need to do their jobs, and close to one-third have made a wrong business decision due to lack of information awareness.”¹ To address this challenge, more and more enterprises are deploying AI in customer service, as it helps to provide more efficient and information-based personalized services.

Technologies such as speech-to-text, text-to-speech, translation, deep learning, transformer models, and generative AI have changed how businesses interact with customers. These technologies enable:

• Real-time analysis of customer feedback
• Automation of customer interactions
• Accurate and personalized AI-based recommendations to assist human agents handle customer inquiries

AI algorithms can process and analyze vast amounts of data, identify customer needs and behavior patterns, and empower the creation of engaging and satisfying customer experiences. Overall, the use of AI in customer service has significantly improved the quality and efficiency of customer interactions, benefiting both businesses and customers.

Join us on June 7 for the webinar Empower Telco Contact Center Agents with Multi-Language Speech-AI-Customized Agent Assists featuring live demos from Infosys, Quantiphi, and NVIDIA.

Language barrier challenges in contact centers

In the global economy, businesses operate across countries and serve customers with diverse linguistic and cultural backgrounds. This global language diversity presents a unique challenge for contact centers.

Effective communication is critical to providing excellent customer service, and language barriers can lead to miscommunication, misunderstandings, and frustration. This can result in dissatisfied customers and missed business opportunities.

Traditional approaches to multilingual support, such as hiring native speakers, training agents in different languages, and providing language-specific scripts are not scalable, cost effective, or efficient.

However, advances in speech AI and translation AI technology are helping contact centers overcome language barriers through language neutralization. This innovation has been crucial for contact centers catering to diverse customers.

What is language neutralization?

In the context of contact centers, language neutralization refers to the process of using transcription, translation, and speech synthesis (TTS) technologies to convert communication from a customer’s natural language to a language that an agent can understand. The agent then responds in their own language, which is again converted through transcription, translation, and speech synthesis, or a combination based on the scenario.

Language neutralization enables effective communication between parties who may not speak the same language, removing language barriers and facilitating smooth interaction. This technique involves advanced AI technologies to equip contact center agents with tools to help them understand customer queries and respond effectively.

Overcoming language barriers

Language neutralization is particularly important for contact centers that provide support services to customers from diverse linguistic and cultural backgrounds. Using language neutralization techniques, contact centers can effectively communicate with non-native speakers and provide them with the same level of service as native speakers.

Infosys Cortex language neutralization powered by NVIDIA Riva

Infosys Cortex, an AI-driven customer engagement platform, transforms contact center operations through purposeful communication and smart decision-making capabilities. With greater brain power and continuous coaching, Infosys Cortex helps employees make better and faster decisions on their journey from new hire to experienced agent.

Infosys Cortex leverages NVIDIA Riva, a cutting-edge speech and translation AI SDK, to power language neutralization capabilities. The world-class accuracy of Riva automatic speech recognition (ASR), neural machine translation (NMT), and engaging speech synthesis empower accurate and natural communication. Based on NVIDIA GPUs for model fine-tuning and processing, Riva enables a high-performance solution for contact centers.

Cortex platform features

The microservices-based architecture of Infosys Cortex includes five key modules that offer the following features (Figure 1):

1. Cortex Core: Sense, analyze, and generate actionable insights from data, and build new customer contexts along the way.
2. Learn: Enable agent training with simulated learning features; based on historical call pipelines, training bank creations, learn-and-practice models, and follow-up actions.
3. Empower: Provide proactive assistance to customers and agents using intelligent nudges based on transaction details, compliance, and real-time sentiment analysis to suggest the next best action.
4. Experience: Integrate with CTI/IVR to create contact flows for self-service, virtual assistance, and intelligent routing to enhance the customer experience.
5. Optimize: Generate insights through analyzing customer sentiment and interaction as well as agent behavior and performance.

Benefits and advantages

Riva services have been instrumental in addressing the key challenges Infosys has faced in relying on contact centers for customer service (Figure 2). The following are some key areas Riva addresses:

• Accuracy: Domain-specific language and product name customizations and fine-tuning different accents and pronunciation enable future-proof solutions.
• Language barrier: Support for 12 languages—Arabic, Chinese, English (US/UK), French, German, Italian, Japanese, Korean, Portuguese, Russian, and Spanish (LATAM/Spain)—with consistent addition of support for new languages.
• Data privacy:  On-premises deployment enables mitigation of data privacy issues, helping to ensure that sensitive data is kept secure.
• Cost reduction: High-performance, efficient Riva models, along with flexible licensing, enable creation of cost-effective solutions as volumes increase.
• Control: Better means of improving Riva models with phonetic boosting and transfer learning for specific domains.

Overall, the advantages Riva models offer over managed services on the cloud include data privacy, predictable pricing, and better performance. In addition, the fine-tuning capabilities of Riva models enable further improvement of the model performance.

Language neutralization requires real-time integration with the CTI audio streams, and latency negatively impacts the experience. Riva on-premises models’ low latency is crucial, as every response must deal with transcription, translation, and synthesis flows at least once.

Key takeaways

Language neutralization is a transformative approach for contact centers, providing a scalable, cost-effective, and efficient solution for multilingual support.

The powerful language neutralization offered by Infosys Cortex and based on NVIDIA Riva speech and translation enables contact center agents to communicate effectively with customers and prevent misunderstandings and ambiguities.

Smoother customer-agent interaction leads to faster handling of issues and a reduction in wait time and backlog. Overall, the reduction in communication-based barriers results in contact centers reducing costs and increasing consistency, thus leading to greater customer satisfaction.

Developers can try Riva containers and pretrained models with a 90-day free trial through NGC. For production deployments, get unlimited usage on all clouds, enterprise-grade support, security, and API stability with the purchase of Riva, a premium edition of the NVIDIA AI Enterprise platform. Learn more.

¹Gartner, Quick Answer: How Should Organizations Prepare for the Addition of Generative AI to the Microsoft Stack?, G00790185, 3/16/2023. GARTNER is a registered trademark and service mark of Gartner, Inc. and/or its affiliates in the U.S. and internationally and is used herein with permission. All rights reserved.