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How do voice assistants work? A look behind the scenes

In recent years, voice assistants such as Siri, Google Assistant and Alexa have become an integral part of our everyday lives. These digital helpers allow us to interact with our devices without pressing a button. But how exactly do these voice assistants work? In this article, we take an in-depth look at the technologies behind these intelligent systems and explain how they are revolutionizing our interactions with technology.

The basics of voice assistants

Voice assistants are programs that are designed to recognize, understand and respond to spoken language. They use a combination of speech recognition, natural language processing (NLP) and machine learning to perform tasks and provide information.

The speech recognition process

a. Speech recognition

The first step in activating a voice assistant is voice recognition. When you speak to a voice assistant, your speech is converted into a digital format.

  • Acoustic modeling: First, the spoken word is converted into acoustic signals. This is done by a microphone that picks up sound waves and converts them into digital data.
  • Feature extraction: The acoustic signals are analyzed to extract important features that are necessary for speech recognition. These features help the system to identify sounds, syllables and words.
  • Decoding: The system then uses a language model to decode the identified sounds into text. Machine learning techniques are used here to improve accuracy.

b. Natural language processing (NLP)

Speech recognition is followed by natural language processing, which enables the voice assistant to understand the meaning of the recognized text.

  • Tokenization: The text is broken down into smaller units, so-called tokens. This makes it easier to analyze and process.
  • Sentence structure and semantics: The language assistant analyzes the grammatical structure of the sentence and identifies the meaning of the words. Methods such as parsing and semantic analysis are used here.
  • Intent recognition: The assistant recognizes the intent behind your request. This is done by comparing the recognized text with predefined patterns and scenarios.

Response generation

Once the voice assistant has understood the intention, the next step is to generate a response.

  • Database queries: The wizard searches relevant databases or APIs to find the required information.
  • Use of large language models (LLMs): Many modern voice assistants use LLMs to formulate the generated response in natural language. These models are trained to generate human-like texts and understand complex questions. LLMs help to create precise and contextually appropriate answers that offer the user a better experience. For example, they could query weather data, news or calendar entries.
  • Answer formulation: The assistant formulates an answer based on the information found. This answer can be written in natural language so that it is easy for the user to understand.
  • Speech synthesis: The generated answer is then converted into spoken language using text-to-speech technology (TTS) so that the user can hear the answer.

Machine learning and continuous improvement

A decisive factor for the performance of voice assistants is machine learning. By analyzing user data and interactions, voice assistants are constantly learning.

  • User data: User interactions are analyzed to identify patterns and improve the accuracy of speech and intent recognition.
  • Feedback loop: Voice assistants use feedback from users to optimize their algorithms and improve the user experience.

Challenges and the future

Despite advances in technology, voice assistants still face challenges:

  • Data protection: The collection and analysis of user data raises questions about data protection. It is important that companies implement transparent data protection guidelines.
  • Dialects and accents: Voice assistants have difficulties understanding different dialects and accents. This represents a hurdle for global acceptance.
  • Contextualization: The ability to understand context and nuances is a challenge for voice assistants. They are often unable to fully grasp the context of requests, which leads to misunderstandings.

Case study 1: Automotive manufacturer for customer service and vehicle information

Company:
A leading international automobile manufacturer

Background
The car manufacturer wanted to improve customer service and offer users an easy way to access information about their vehicles. Customers often had difficulties finding important information such as operating manuals, maintenance instructions or technical specifications.

Solution
Implementation of a voice assistant for mobile and smart home devices

  • Real-time interaction: The voice assistant accesses the vehicle data to provide personalized responses based on the specific model and previous maintenance work.
  • Technology: An AI-powered voice assistant has been developed that is integrated into the company’s mobile app and is also accessible via smart home devices such as Amazon Echo and Google Home.
  • Functions: The voice assistant allows users to ask questions such as “How often do I need to change my oil?” or “What safety features does my vehicle have?”. It can also send maintenance reminders and provide information on dealer locations.

Result
Following the introduction of the voice assistant, the car manufacturer was able to increase customer satisfaction by 35%. Customers appreciated the immediate availability of information and the user-friendly interaction. The company received positive feedback on the user-friendliness and efficiency of the customer service.

Case study 2: Healthcare provider for patient interaction

Company: A large healthcare company with numerous clinics and practices

Background
The company was confronted with a high number of calls and requests for appointments and general information. This led to long waiting times for patients and put a lot of strain on the staff.

Solution
Development of an intelligent voice assistant to support patient interaction

  • Technology: A voice-activated system was implemented, which is available both on the company’s website and via telephone calls.
  • Functions: The voice assistant enables patients to make appointments, call up information on services and answer frequently asked questions. For example, patients can say: “I would like to make an appointment for an examination” or “What vaccinations do you offer?”
  • Integration with existing systems: The assistant is integrated with the appointment management and patient management system, allowing for seamless booking and management of appointments.

Result
The voice assistant reduced the call load in customer service by 40 % and increased the number of successfully booked appointments by 25 %. Patients reported a better experience as they received immediate responses to their queries and waiting times were significantly reduced. This led to improved patient retention and a more efficient operation.

The integration of voice assistants into our customers’ business processes is more than just a technological innovation – it is a step towards a more efficient and customer-centric future. Through our tailored solutions, we enable companies to provide real value to their customers by simplifying interactions while gaining valuable insights into user behavior. We are proud to actively support our customers’ transformation and help them succeed in an increasingly digital world.

Till Neitzke

Outlook and conclusion: Voice assistants – a look behind the scenes

The future of voice assistants looks promising. With advancing technologies in machine learning and artificial intelligence, voice assistants are expected to become smarter, more user-friendly and contextualized. Integration with different devices and platforms will allow users to interact seamlessly with technology wherever they are.

Voice assistants have revolutionized the way we interact with technology. By combining speech recognition, natural language processing and machine learning, they enable intuitive and efficient communication. While there are still challenges to overcome, the technology is showing promising progress that will lead us into a future where voice assistants are indispensable companions in our everyday lives.

Voice tech explained: The most important questions about voice assistants

A voice assistant records spoken language via a microphone, first converts it into text (speech-to-text), analyzes this text using artificial intelligence and determines the intention behind the statement (intent recognition). Based on this analysis, the assistant generates a suitable response, which is either read out or displayed. Depending on the system, it can then also perform actions – e.g. open an app, switch on the light or create a calendar entry.

Several complex AI modules work together in the background:

  • Speech recognition (ASR – Automatic Speech Recognition):
    Recognizes spoken words and converts them into written text.
  • Natural Language Processing (NLP):
    Analyzes the text, recognizes keywords, grammar and the intent behind the statement.
  • Dialog management:
    Decides how the assistant should react to an input. It controls the conversation and “knows” what was previously said.
  • Response generation (NLG – Natural Language Generation):
    Creates the actual response – either from predefined blocks or dynamically generated.
  • Text-to-speech (TTS – speech synthesis):
    Converts the answer into spoken language so that it can be read aloud.

These processes often run within fractions of a second – either locally on the device or in the cloud.

Both use similar technologies for speech processing, but they differ in their interface:

  • Voice assistants work primarily via spoken language.
  • Chatbots are mostly text-based – e.g. on websites, in apps or in customer service.

From a technical point of view, voice assistants need additional modules for audio processing (e.g. microphone control, voice output) and advanced conversation models, as spoken language is often more informal and error-prone than written text.

Many modern assistants use machine learning to continuously improve. This means that the more often a user interacts with the system, the better it understands individual speech patterns, preferences or recurring commands.
Example: If you regularly say “Turn on the living room light”, the assistant can learn that “living room” is your preferred room – even if you later only say “light on”.

The following applies: Learning only works if usage data can be analyzed – depending on the settings, privacy policy and system.

Understanding dialects, accents or regional language is one of the biggest challenges for voice assistants.
Large providers train their speech recognition systems with huge amounts of data that cover many speech variants. Nevertheless, errors can occur if certain pronunciations deviate from “standard German”.
The better the system has been trained – and the clearer the speech – the higher the recognition accuracy.

As a general rule, voice assistants are as secure as the platform on which they run – and how consciously you use them.
Many assistants only activate the microphone when they hear a ‘wake word’ (e.g. ‘Hey Siri’, ‘Alexa’). The actual voice analysis usually takes place in the cloud, which means that data leaves your device.
Pay attention to whether and for how long voice data is stored, which settings you can change and whether you are allowed to delete or manage recordings.

Voice assistants do not usually listen actively all the time, but wait for an activation word. A voice recording is only started after the wake word and sent to servers for analysis.
Some providers save these recordings by default – either to improve the systems or for quality assurance. However, you can often specify in the settings that no recordings are saved or that old recordings are automatically deleted.
Transparency and control over your data are important criteria when choosing a system.

Voice assistants work with statistical probabilities. If the voice input is unclear, ambiguous or taken out of context, the system can misinterpret the intention.
Many assistants also reach their limits when it comes to technical terms, irony, sarcasm or complex questions – especially if they have not been trained for this.

A wake word is a defined word or phrase that “wakes” the voice assistant. Active speech recognition and processing only begins after this word.
Examples:

  • “Hey Google”
  • “Alexa”
  • “Hey Siri”
  • “Hello DMG”
  • “Computer” (e.g. for user-defined systems)
    The wake word is processed locally – i.e. on the device itself – to ensure data protection and performance.

Yes, it is possible! There are open source projects such as Mycroft, Rhasspy or Leon that you can use to develop your own voice assistants – e.g. for smart homes, business solutions or individual applications.
However, the effort involved should not be underestimated. You need expertise in areas such as AI, audio processing, server infrastructure, data protection and user experience. However, there are also low-code/no-code platforms or commercial tools for simple applications.

The best known systems are

  • Amazon Alexa – very strong in the smart home and skills sector
  • Google Assistant – known for contextual understanding and search functions
  • Apple Siri – closely integrated with the Apple ecosystem
  • Microsoft Cortana – now discontinued in the consumer sector
  • Samsung Bixby – focused on Samsung devices
  • ChatGPT Voice – with natural, dialog-oriented AI in voice mode

Each system has its own strengths, restrictions and data protection guidelines. A comparison is worthwhile!

Yes, we develop customized voice solutions – from simple voice interfaces to complex, multimodal voice assistants. We know the ins and outs of voice input, dialog guidance, voice output and integration into existing systems.

Absolutely. We develop individual Alexa skills, actions for Google Assistant (if desired) and voice interfaces that integrate into various ecosystems. We also offer platform-independent voice solutions, for example for websites, apps or embedded devices.

We start with a joint voice strategy workshop to clarify target groups, use cases and requirements. This is followed by

  1. Dialog design & prototyping
  2. Technical implementation (incl. NLP & TTS)
  3. System integration & testing
  4. Rollout & user feedback
  5. Maintenance & further development

Depending on the use case and data protection requirements, we work with:

  • Google Speech Services
  • Amazon Polly & Alexa Voice Services
  • Microsoft Azure Cognitive Services
  • Open source alternatives (e.g. DeepSpeech, Coqui, Rhasspy)
  • Custom models (e.g. Whisper + GPT) for private or data-sensitive applications

Yes, we realize completely independent voice assistants that run independently of the major tech platforms – e.g. in your app, on terminals, in vehicles or devices. Ideal for companies that want full data sovereignty, CI-compliant UX and individual functions.

Data protection is particularly sensitive for voice assistants. We offer:

  • On-premises or GDPR-compliant EU hosting
  • No permanent listening – wake-word based or button-controlled
  • Transparent data processing and deletion routines
  • Advice on audio recording, logging and opt-in procedures

In any case. We develop context-based, natural dialogs, pay attention to speakable language, clear feedback and intuitive conversation. This also includes:

  • Voice personas & tonality
  • Fallback strategies
  • Multimodal interaction (e.g. speech + screen)

Yes, whether CRM, ERP, smart home, IoT or internal databases – we take care of the integration via API, webhook or middleware so that the voice assistant creates real added value, e.g. through access to live data, automation or system control.

Definitely. For example, we implement voicebots for internal self-services in HR, IT or facility management. Also on devices such as tablets, in apps or in production environments – voice control is becoming increasingly relevant internally, especially hands-free or for non-desk employees.

We combine technical excellence with end-to-end KPIs and user-centered voice design. Our projects are modular, data protection-compliant, future-proof and concrete. And: we really listen before we get started.

Successful together in the digital transformation –
Your introductory meeting with DMG

In our introductory meeting we will discuss

  • what your current challenges are in digital projects
  • how other companies have done it and how you can use this to your advantage.
  • what needs to be done now and whether we are the right people for the job.