Voice-Based AI for Health Applications: Implementation Guide

With the widespread usage of text-based AI solutions such as chatbots or AI assistants, a voice interface seems like a natural extension. Although this move is an instinctive one, and what is more, an often taken path, we do not see many successful voice-based AI applications, especially operating in the delicate medical environment. This boils down to a simple truth - voice processing is based on multiple technical nuances that, when unaddressed, might lead to the system being at best cumbersome, and at worst unusable.

Natural Speech Flow: The Issue of Fluency

A voice interaction is, barring cases of recorded messages, by nature, a real-time interaction. This most fundamental thing about voice interfaces might seem obvious, but it leads to multiple consequences that dictate the technical requirements.

Let’s analyse text-based and voice-based exchanges. With text, we interact with the full user message—the text is only sent after the user stops typing and submits the message. This gives us the luxury of processing a semantically complete block of data and, what is more, creates no expectations of an instant reply. After all, in a natural chatting experience, the human on the other end must read the message and type the response back.

With a voice, this simple schema is entirely thrown out. When we listen, we naturally process the language as it is spoken. This makes the response fast and snappy, leaving little room for long pauses in a naturally flowing conversation. Most audio processing systems are unable to work this way. We must input a complete message into a model for it to be able to make an inference and generate a response. Inputting half a sentence, and then the missing one leads to a jumbled mess, as the model is only able to work on a batch input base - any input streamer into a model is usually still aggregated into input frames of set length before processing. Due to this limitation, we already start with unnaturally long pauses, and this is not considering the inference and generation process itself.

Such pauses are instantly off-putting to the user, shattering any semblance of conversation. In some cases, this batch processing is acceptable. Think of a notetaking system, or a command-activated one, where there is no direct expectation of immediate response.

Hurdles of Real-Time Audio Processing

Moving from the issue of pauses alone, we need to consider the nature of the data that we are processing. After all, text data is a purely digital input - converting it from text representation into vector representation associated with modern NLP systems or LLM models, while not trivial, is a rather direct transformation. Classic signal processing tasks.

Let's start with the assumption that the act of audio recording is handled for us by an already implemented voice capture interface. This removes any issues related to pure signal recording and digital representation, but we are not safe yet.

While some models support a direct stream of data, usually this is not the case, leading us to buffer the incoming data into audio frames - segments of data corresponding to assembled raw audio bytes. The representation of the audio is important as well - a widespread format in ML is a MEL spectrogram.

While this might be handled by your chosen audio handler, it still needs to be kept in mind to know the nature of the signal you are working with.

Game of Chinese Whispers

The first problem that we need to tackle is the signal denoising, or, to be clearer, removing any unwanted noise and recording artifacts. In this case, by noise we mean any audio introduced either by the recording and transmitting process itself or bleeding over from the background. Naturally, the noisier the background, the harder the task will be, same as with the lower quality signal we receive, the harder it will be to process.

Lots of strategies are available on this stage, from simple low and high pass filters, through signal denoising ML methods and dedicated autoencoder neural networks - the key takeaway here is that the process is never lossless. Each denoising is potentially degrading the speech hiding underneath, and at a certain level of noise, the speech is simply irrecoverable.

Speech or Not?

Next in line is identifying whether the incoming signal is speech at all - sending a constant stream is not only nonoptimal, but also leads to highly unstable model behaviour, seeking speech patterns in pure noise.

For this task, we usually defer to voice activity detection models - VADs. Those methods vary greatly, from simple, algorithmically calculated, energy-based thresholding up to dedicated neural networks able to separate speech occurrences. Depending on the desired accuracy, available resources, and computing power, selection of a VAD model might be different, but the goal is the same - we want to receive a separated speech block.

An important distinction here is whether to locate the VAD server-side or client-side. For conversational chatbots, a server sided VAD might be a good fit, as we have the luxury of higher computing power and assume a long intensive interaction, but for simple voice activated interfaces (think here of assistants such as Alexa) we would be sending a redundant and constant stream of noise, only to occasionally select few phrases. Here, a client-based VAD is more suitable.

Speaker Diarization

A problem that we face only in some cases, but might prove to be rather difficult, is multiple speaker handling. Usually, it is safe to assume that all incoming speech should be treated the same, but for some edge cases like notetaking during a medical interview, preserving the information of who is speaking is crucial.

This problem is called speaker diarization and can usually be solved by a pretrained neural network, optimized for signal processing, working frame by frame, and flagging each frame with metadata signifying which person is speaking. This usually requires the model to preserve each new occurrence of a speaker for the context of the current conversation to be able to determine whether an additional person is speaking, and to maintain data labels identifying separate speakers through the recording.

All Roads Lead to Text

Here, after all the trouble we went through, usually, we face the hard truth - in order for our model to most efficiently work with the audio, it will need to convert it into text.

This statement needs to be prefaced with a clarification. This stage will not be necessary if you want to analyse the voice sample itself, and not the words being spoken themselves, or when you use a multimodal LLM, able to process a direct audio stream, but it is still very common for any voice-based interfaces for LLM chatbots.

For this, we once again use a dedicated submodel, this time a speech-to-text model. With that done, we can finally pass the text transcription to the model for inference.

And From Text - to Speech

Finally, we arrive at the final junction, the conversion of the text generated from the LLM model into audio. Once again, this step is optional and might be skipped by using a multimodal LLM that synthesizes a direct audio response. For other cases, a text-to-speech model will need to be employed.

In simple applications, a classic voice synthesizer will suffice. Albeit a clear artificial voice, it is far easier to generate than a sophisticated and realistic imitation generated from dedicated GAN neural networks working in text-to-audio mode.

Universal Obstacle: Model Bias

As with any ML systems, a universal problem, especially crucial for medical environments, is medical bias and good data representation. For audio, this problem will show itself the most with underrepresented patient groups - this covers anything from non-native accents, different languages, and up to speech impediments, which can, depending on the medical environment, be a real issue. Models trained on standard speech samples might not handle recordings coming from patients who, due to any reason, are unable to speak clearly.

Those are not simple issues, and while problems such as language representation or accent handling are solved easily enough with models that were trained on and support the expected language scope, issues rooted in medical conditions might require special handling, up to a custom-trained and fine-tuned model.

In the Pursuit of Fluent Speech

After walking through all the stages of audio processing, the problem of generating a fluent conversation is probably more than clear, but all is not lost - after all, we see more and more systems that despite all those issues prevail and deliver a working solution.

The core issue is to assess the exact environment your voice solution will be operating. Depending on the exact use case, not all the processing stages are needed. Steps such as speaker diarisation or denoising are only needed in special applications. Furthermore, by using the aforementioned multimodal LLM models that, despite being more complex, offer an overall more fluent experience by cutting down the transformations needed to perform.

Lastly, you can consider building solutions that work less interactively, while still supporting your patients or medical personnel. Think about command-activated interfaces, voice recording and analysis or transcription tools - the possibilities are endless.

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Voice Interaction Possibilities

To close off on a positive note, voice interface is not merely a convenience feature - it opens up new possible interaction path for users in different situations, from notetaking and automated execution tools supporting specialists during delicate tasks and operations, acting as a human assistant taking notes would, to an accessibility feature, enabling patients with limited vision or motor skill to interact with multiple systems.

A few avenues that AI models handling speech processing currently thrive in are not directly linked to conversational agents. We see audio processing models being successfully utilized for text transcription, often leaving the medical practitioners to fully focus on the patient. This is not only limited to medical interviews, such tools can be successfully used in therapy sessions, creating not only a text record of the session, but also an audio recording for offline analysis of speech patterns, inflections, and other features.

Another important road is accessibility and tools enabling patients with limited sensory or motor capabilities to be able to interact with the word - either through a voice interface, an interactive agent, or tools as sophisticated as real time audio conversion into desired format.

The medical applications of audio processing and voice handling are plentiful, but the key to successfully building a healthcare application utilising them is to know the available tools - their strengths and limitations, and apply them accordingly.

Final Thoughts

Designing voice-based AI for healthcare is not just a technical challenge—it’s a matter of clinical usability, patient trust, and accessibility. The path from raw audio to seamless interaction is filled with decisions that shape the quality, safety, and inclusiveness of the end experience.

While no one-size-fits-all solution exists, understanding the building blocks—speech detection, diarization, transcription, synthesis, and bias mitigation—gives product teams the clarity to design systems that genuinely work in real-world healthcare settings.

If you're exploring how voice can enhance your health application—whether through accessibility, automation, or hands-free workflows—start with the right architecture. We can help you evaluate what your product needs, and what it doesn’t.