Products with voice processing

Alternatives for developing products with voice processing. Advantages and costs.

Introduction to the development of voice-enabled products

Voice-enabled products are devices that, regardless of their functions and objectives, have the ability to receive commands and modify their behavior through the reception and interpretation of voice commands. 

The development of voice-enabled products is a clear opportunity to enhance the relationship between users and machines. The inclusion of voice processing in products can serve a wide range of purposes:

  • Enhance the user experience when interacting with products. 
  • Modify the function of products in scenarios where interacting with screens or buttons is impossible or complex.
  • Reduce the costs of operations that are intensive in human resources. 

Below, we’ll look at some examples of products where voice-controlled processing adds significant value to the user experience, as well as the main alternatives we have explored at Let’s Prototype to integrate voice processing into prototypes.

productos con procesamiento de voz

How to choose the right voice processing technologies for new products

Before analyzing the alternatives we use in our invention lab to integrate voice processing into innovative products, it's essential to understand the balance between: processing freedom vs. development costs and timelines.

At the most complex end of the spectrum, we find products capable of listening, interpreting, responding, and customizing functions—even in noisy environments where dialogue is more challenging. On the opposite end, we have simpler products with basic voice recognition, designed to perform specific, predefined functions in response to very concrete voice commands. 

Precisely, the technological alternatives for creating voice-enabled products must be chosen based on the operational needs of the innovative products being developed.

Alternatives for creating innovative products with voice processing.

There are four main alternatives for integrating voice processing and functional execution into a product. These options vary depending on the product’s functional requirements. 

Need

Suitable alternative

Costs

Listen + perform function

Use of hardware solutions. Chips with predefined models.

$$

Listen, perform function, improve model behavior.

Recognition models created on no-code platforms. 

$$$

Listen, interpret, respond, perform functions, improve model behavior, enable data traceability, and customize structure. 

Custom development and training of neural networks. 

$$$$$

Listen, interpret, perform functions, respond.

Integrations with natural language AI solutions. 

$$$$

1. Inclusion of chips or hardware components with embedded models

At Let’s Prototype, we frequently work with hardware components that are well-established in the market as stable and accessible solutions, allowing control through technologies that are fully compatible with electronic prototype development.

Voice recognition chips are small electronic components capable of identifying very specific voice commands, which can be configured through a platform without the need for extensive development.

Main advantages of voice recognition chips.

  • They do not require significant investment in AI model development and training.
  • Quick integration into electronic designs compatible with electronic prototypes.
  • They usually offer comprehensive and user-friendly development kits for command tuning. 
  • High versatility in defining voice commands. 
  • They do not require connectivity.
  • Their size has minimal impact on the planned geometries of the product's electronic design. 
  • Their cost is very low, making them a viable investment for the rapid prototyping process.

Main disadvantages of voice recognition chips.

  • They create a certain level of dependency on the manufacturers of these hardware components 
  • Limited list of voice commands that can trigger specific product functions. 
  • They do not interpret dialogues or enable human-like communication.

Examples of products with voice recognition using chips.

Glasses for surgeons and dentists: Protective glasses used in operating rooms, also common among dentists, often include a lighting system controlled by buttons. Due to hygiene concerns and restricted movement—typical in these medical procedures—using buttons can be tedious and ergonomically inconvenient. The integration of voice processing chips into this type of product could significantly improve the user experience. Imagine using simple commands like “on” or “off,” which could be recognized by the device to eliminate the need for physical contact with the glasses.

Smart security devices: There are numerous products on the market designed to ensure the safety of individuals who may be at risk of harassment or dangerous situations. At Let’s Prototype, we have developed devices where potential danger can be detected through simple voice commands, which in turn activate the logic for sending alerts or notifications.

Smart TVs: Many modern home appliances now include voice command recognition chips. In this case, we’re not referring to the ability to listen and search for content, but rather to specific operations based on predefined formats, such as turning the TV on or off, or adjusting the volume up or down. Content search and playback, instead of being handled through predefined commands or voice recognition models, is usually managed via voice-to-text conversion systems that feed into content platforms.

2. Voice recognition products using no-code platforms.

In this context, no-code platforms are user-friendly environments that allow product development teams to train neural networks using visual tools. These platforms include highly practical features that streamline the AI model training process.

Unlike chips that enable the use of predefined voice commands, these platforms allow for the creation of more complex listening and interpretation scenarios. Additionally, they eliminate dependency on specific electronic components from particular suppliers who commercialize such solutions. 

In our view, beyond the complexity of the resulting AI models, a key difference is that with voice command chips, the intelligence layer can never be transferred to our own electronic control software. In contrast, voice processing models trained on no-code platforms can be used and integrated directly into the custom firmware developed for the product’s electronic control system.

In other words, when using chips, the intelligent voice recognition model can never become part of your software solution. In contrast, when training neural networks using no-code platforms, you obtain optimized models that can be integrated into your solution, offering greater flexibility and control.

Advantages of voice recognition models trained on no-code platforms.

  • They offer user-friendly environments with a manageable learning curve. 
  • Support from educational, visual, and intuitive tools.
  • Efficient neural networks can be generated and integrated into custom development, even when using standard electronic components commonly found in prototyping. 
  • Easily exportable to devices with processing capabilities that do not require internet access. 
  • Allows continuous training and improvement of the model’s efficiency over time. 
  • Licensing costs are not excessively high.
  • These tools are well-documented and usually supported by reliable support teams. 

Disadvantages of voice recognition solutions trained on no-code platforms.

  • The performance of these models is often poor in noisy environments. 
  • The customization of the neural network is limited, as the model’s code is not accessible. 
  • There is a certain level of dependency on the creation platform, especially for model expansion or retraining processes.

Use cases of voice recognition models created on no-code platforms.

Examples of voice recognition products that require activation through simple commands are ideal for development on no-code platforms. At Let’s Prototype, we use this type of voice recognition model or the previously mentioned chips depending on variables that affect not only the product’s technical feasibility, but also the viability of the future business model, time to market, available resources, and more.

Voice-enabled sports wearables: In devices with certain size and power constraints, where voice command interpretation was required for the point scoring system, we chose to use models trained on no-code platforms.

Baseball wearable: For example, in a smart bracelet for baseball, users can announce the type of swing they are practicing using voice commands, and the bracelet performs real-time evaluations of those movements. It reports the percentage of similarity between the user's actions and professional patterns stored within the device. Additionally, it recognizes commands to count laps during running workouts, among other specific functions that can be activated or deactivated using simple voice commands.

Orange juice machine with voice commands: At Let’s Prototype, we developed an orange juice machine that, once the juice is prepared, can start and stop self-cleaning processes using voice commands. Moreover, the machine is capable of understanding commands in multiple languages and recognizes a variety of synonyms. In this case, the integrated electronic solution does not require any specialized chip or hardware component that would increase mass production costs. It also does not rely on internet connectivity, recurring license fees, or model retraining for new versions.

3. Training voice recognition models from scratch for innovative products.

In certain products, voice processing requirements go beyond the basic functions of listening, identifying, and executing. These are complex technological products where a much more fluid “machine–human” communication experience is essential. In such scenarios, it is crucial to develop or continuously evolve the model, ensure traceability of the training data, maintain transparency in how the model works, and retain full control over its behavior. For this kind of voice-enabled device involving complex command analysis, pre-programmed voice recognition chips and no-code software solutions for training neural networks are insufficient.

Examples of products with voice recognition using complex models.

Voice-enabled robots for the healthcare sector: At Let’s Prototype, we had the opportunity to participate in the development of a robot designed to provide support during triage processes in hospitals, specifically in emergency departments. The robot is capable of engaging in conversations and, within this context, capturing useful data to assist in deciding which type of consultation patients should be directed to. The robot’s communication capabilities through dialogue, along with other parameters, allow it to assign urgency levels.

Advantages of custom voice processing models for products.

  • Ability to customize the model with the highest level of detail. 
  • Possibility to customize the data architecture and parameters. 
  • Voice recognition models can evolve and scale without relying on chip manufacturers or no-code platform providers. 
  • Allows complete transparency and traceability of the data that influences the neural network. 
  • Ready to operate without connectivity. 

Disadvantages of custom voice processing model development.

  • High technical barriers.
  • High development and neural network training costs.
  • Maintenance costs.
  • Integration into custom solutions can be challenging. 

4. Products with voice processing capabilities through integrations.

Widely available natural language solutions are a significant asset in the development of tech products that need to engage in coherent conversations with users. As with the previous case involving neural networks trained from scratch and full flexibility, the goal is to sustain dialogue, listen actively, and make decisions based on complex interpretations depending on the context.

Although the objectives may be similar, when there are no data traceability restrictions, a moderately complex solution could involve using pre-trained natural language AI solutions. Google, ChatGPT, Amazon, and Azure are among the most commonly used options.

Traceability and analysis of the logic used to generate responses in these cases are much less controlled. This factor is critical when deciding whether it is appropriate to develop a custom model or to integrate existing AI solutions.  

Robots capable of assisting users in retail stores—by analyzing preferences, trends, and matching them with available offerings—are a clear example of the potential of new products with voice command processing. These systems help humanize the interaction and achieve predefined goals based on the interpretation and learning acquired through their interactions with people. 

At Let’s Prototype, we are currently developing a robot designed for restaurant environments, with the main goal of enhancing the user experience regardless of the customer’s language. We hope to add it soon as an update to this page.

Prototypes with Voice Processing.

The development of a prototype with voice processing capabilities and command execution can range between $25,000 and $50,000. To accurately estimate the cost of creating a voice-enabled product, it is essential to fully understand the technical requirements of the invention.

There is no single best technological alternative. The most suitable option depends on the specific requirements of each use case and should be carefully evaluated. That said, training neural networks for voice recognition using no-code platforms remains the most commonly used approach in our prototype lab.

The design and development process of a prototype capable of listening to commands and performing specific functions typically takes between 4 and 6 months.

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