Accueil
Blog
Best network architecture for speech recognition software

Best network architecture for speech recognition software

Best network architecture for speech recognition software
Published on
Mar 2024

Building high-quality speech recognition software for your businesses has never been easier. But one needs the right infrastructure to make the most out of AI transcription at an enterprise scale.

Given the increasing commodification of automatic speech recognition models and APIs, companies today are presented with numerous options on how to build and deploy their AI-powered systems and apps.

Network architecture is the foundation of one's operational efficiency, security, and cost optimization. Companies that want to integrate Speech AI into their tech stack need to decide where they want the underlying network infrastructure to be located, and who they want to own it, while taking into account the specific requirements associated with speech recognition tech.

In this blog, we give you a quick overview of key alternatives - cloud, on-premise and air gap - to help you take an informed decision on which kind of environment is best suited for your needs given your use case and security needs. Bear in mind that Gladia provides all types of hosting for speech-to-text to power enterprise applications. To learn more, contact us directly about the enterprise plan.

Network architecture for speech recognition: key factors to weigh

Speech recognition, also known as speech-to-text, software may present unique challenges for businesses, demanding specialized considerations beyond traditional hosting and deployment needs. These include the immense processing power and speed required for real-time transcription, bandwidth considerations for handling large audio datasets, and the need for scalable storage solutions. Let's examine some of these in more detail.

Real-time factor

Real-time, or live, transcription is an indispensable feature found in voice-based apps like chatbots, media platforms with live captions, and more. As explained in our deep dive on the topic, real-time transcription requires substantial processing power to convert audio signals into accurate output in near real-time. While proximity to the source can be a great advantage for latency in live streaming, top-tier cloud-based API providers can do the job just fine remotely, provided that efficient parallel processing capabilities and a WebSocket support are in place to ensure smooth bidirectional flow of information and fast processing.

Bandwidth and scalability

Audio datasets can be voluminous, especially in applications dealing with continuous speech or a large number of audio inputs - like customer support and call center operations. Adequate network bandwidth, with suitable compression techniques and optimized data transfer protocols, is essential to transmit large audio files seamlessly, especially in real-time applications.

Storing and managing large volumes of audio data generated by speech-to-text applications requires scalable and efficient storage solutions, too. When deciding on a network environment for audio data, one must anticipate how to accommodate the growing volume of audio data. As explained below, on-premise hosting allows for less flexibility when it comes to scaling in exchanged for increased security.

Security and certification

Speech-to-text applications often deal with sensitive information, raising concerns about data security and privacy. Some use cases and industries require specialized certification and full data sovereignty, with encryption becoming a standard practice whatever the field.

Key types of hosting in speech recognition

1. Cloud multi-tenant (SaaS)

With multi-tenant cloud environments, all users share the same hardware and software, as well as the same instance of the software, provided by a third-party provider that oversees everything from installation to maintenance and software upgrades.

This is the most scalable hosting solution, enabling your company to easily add more users and scale the volume of audio on a pay-as-you-go basis. Regular software updates come as part of the package, with no additional maintenance or upkeep costs. Cloud environments also provide seamless integration with AI and ML services, enhancing the accuracy and efficiency of speech recognition systems.

Like with any third party solution, potential safety hazard in case of a cloud security breach may make this option less suitable for industries with strict privacy and compliance protocols. Also, while flexible tariffs can be very attractive, users should be mindful of processing and storage costs, ensuring they align with the application's usage patterns.


2. Cloud single-tenant

Similar to multi-tenant, except that there's a dedicated cloud infrastructure per client, managed by an external provider, with each user having access to their own instance of the software.

Higher level of security since the virtual network is reserved for a single user.
Better governance.


Higher costs. Also, as with multi-tenant, data security and privacy is dependent on the provider's certifications and capabilities.

3. On-premise

On-premise environments, also known as in-house hosting, refers to the deployment of computing resources within an organization's physical location. This includes servers, storage, and networking equipment that is owned and maintained by the organization. Licensed software is hosted on client-controlled data centers, i.e. an exclusive physical and virtual network. The environment tends to be managed by the company’s IT department or, less commonly, a third-party provider. 

Data sovereignty, i.e. the user retains full control over what happens to enterprise data.

Significant upfront deployment costs and CAPEX. The uptime can also be impacted significantly in case of hardware failure since, unlike in the cloud, there’s no safety net to fall back on. Moreover, service-level agreements (SLAs) and commitments need to be managed internally.


4. Air gap

Air gap hosting is an extreme form of network security where a computer or network is physically isolated from all third party networks - including the internet.


Isolation from external networks minimizes the risk of unauthorized access, providing optimal level of protection for high security facilities with stringent internal protocols, like government and military institutions.

Lengthy time to recovery in case of a local issue (such as natural disaster or business interruption). If the hardware is down or the software needs an upgrade, physical intervention from a certified provider would still be required. Air-gapped environments come with a high cost of maintenance, with roughly the same high CAPEX as on-premise.

Speech-to-text hosting: the security-scale tradeoff?

In a nutshell, the further we move from 1 to 4, the higher the level of security – but there’s a price to pay (and not just in $$). Beyond significant deployment and maintenance costs, companies hosting on-premise are restricted to the capacity they’ve committed to initially. In other words, they sacrifice the ability to scale.

While the network latency is likely to be better on-premise than in-cloud, that only holds true if their servers are not saturated with users. Should the initial capacity accounted for be exceeded, there’s a lot less room for scaling than with a pay-as-you-go cloud solution— unless one is ready and able to invest in more hardware to scale.

What’s more, security doesn’t need to be compromized when opting for cloud services. As a user, you have a right to verify that a third-party provider meets all the regulatory and security requirements with the necessary certification and beyond. Add-on features like encryption and anonymization can provide an additional degree of security to duly protect your and your customers’ data when working with an ASR API.

Taking stock, when deciding on a hosting architecture for speech-to-text applications, we recommend basing your choice on the following criteria.

  • Security and privacy: Assess the level of security required for your speech data, especially if dealing with sensitive information.
  • Real-time processing: Consider the real-time processing needs of your application and the tolerance for latency.
  • Budget constraints: Evaluate your budget constraints and determine the cost-effectiveness of each hosting option based on the volume of audio and the nature of your use case.
  • In-house staff: When hosting on-premise, you need to ensure the team is equipped to deal with potential scaling and downtime instances.
  • Regulatory compliance: Ensure compliance with industry-specific regulations governing speech data processing.

At Gladia, we accommodate all types of enterprise needs, with cloud, on-premise, and air-gap environments all available as part of our Enterprise plan. Feel free to sign up directly below if you want to test the API or contact our sales team directly here to discuss the plan.

About Gladia

At Gladia, we built an enhanced and optimized version of Whisper in the form of an API,  adapted to real-life professional use cases and distinguished by exceptional accuracy and speed of transcription, extended multilingual capabilities, and state-of-the-art features.

To learn more about Gladia’s approach to enhancing the Whisper transcription performance for companies, check out our latest model, Whisper-Zero.

Contact us

280
Your request has been registered
A problem occurred while submitting the form.

Read more

Speech-To-Text

OpenAI Whisper vs Google Speech-to-Text vs Amazon Transcribe: The ASR rundown

Speech recognition models and APIs are crucial in building apps for various industries, including healthcare, customer service, online meetings, and entertainment.

Speech-To-Text

Best open-source speech-to-text models

Automatic speech recognition, also known as speech-to-text (STT), has been around for some decades, but the advances of the last two decades in both hardware and software, especially for artificial intelligence, made the technology more robust and accessible than ever before.

Case Studies

How Gladia's multilingual audio-to-text API supercharges Carv's AI for recruiters

In today's professional landscape, the average workday of a recruiter is characterized by a perpetual cycle of administrative tasks, alternated by intake calls with hiring managers and interviews with candidates. And while recruiters enjoy connecting with hiring managers and candidates, there’s an almost universal disdain for the administrative side of the job.