Modern smartphones no longer rely on a single simple microphone to capture sound. Most phones have multiple microphones placed in different parts of the body, each picking up ambient sound from a slightly different position.
That small difference provides valuable information to the phone's audio system. When a person speaks, their voice reaches different microphones with differences in time and intensity. The phone's processor can use these differences to estimate the direction of the sound source and determine which signal is likely from the primary speaker.
How do multiple microphones help remove interfering sounds?
Imagine you are talking on the phone in the street. Your voice arrives at the phone from one direction, while the sounds of cars, other people, and other audio sources come from different directions.
The phone can compare the information received by the different microphones. This makes it possible to amplify the signal coming from the desired direction and reduce signals entering from other directions.
One important technology at this stage is beamforming. In this method, the system uses an array of microphones to steer its focus toward a specific audio source. As a result, the desired speaker's voice becomes more prominent relative to some of the surrounding noise.
Of course, beamforming alone cannot remove all interfering sounds. For this reason, another stage of processing begins after the sound is captured.
Digital processing comes into play
The raw signal that the microphone receives is a mixture of the speaker's voice and various environmental sounds. The phone's audio processor analyzes this signal to reduce unwanted noise.
Some sounds are relatively easier to identify. For example, the steady hum of an air conditioner, a car engine, or constant background noise can be largely reduced using signal processing methods.
But the problem becomes more complex when environmental noise resembles human speech; for example, when several people speak simultaneously. In such situations, the system must examine more complex audio features in addition to sound intensity and direction.
Artificial intelligence helps the phone
In newer phones, machine learning also plays an important role in audio processing. Acoustic models can be trained using very large samples of speech and noise to better distinguish between human voice and interfering sounds.
These models can help the system identify parts of the signal that are likely to contain speech and reduce the noise within them. In some more advanced technologies, neural models can even attempt to extract one person's speech from among multiple speakers or very noisy environments.
The goal at this stage is not necessarily to produce audio that sounds completely natural to the human ear; rather, in many applications the main objective is to produce a signal that a speech recognition system can understand better.
Does the phone recognize your voice?
Here we must distinguish between two different concepts: speaker separation and speaker identification.
When a phone tries to separate your voice from the sounds of cars or people around you, it does not necessarily know who you are. At this stage it only attempts to extract the desired speech from other sounds.
But some features can use the unique characteristics of a voice to identify or verify the speaker. This capability depends on the type of system and its application.
Therefore, when your phone's voice assistant correctly understands your command in a noisy environment, it does not necessarily mean the phone has identified you; it may simply have successfully separated your speech from the noise.
From air vibrations to becoming text
After processing and partial cleaning of the signal, the automatic speech recognition stage begins. In this stage, the audio system tries to map the pattern in the waveform to linguistic units and then to words.
For example, when you dictate a message, the overall path is as follows: the microphone captures the sound, audio processing reduces the noise, speech-related algorithms analyze the signal, and finally the speech recognition system converts it to text.
All these stages occur in a fraction of a second, which is why the user usually does not notice the complex chain of processing that happens between saying a sentence and seeing it appear on the screen.
The phone is not actually a single "ear"
Consequently, the ability of smartphones to hear the user's voice in noisy environments is not the result of a single technology. Multiple microphones, microphone arrays, digital signal processing, beamforming, noise cancellation algorithms, and machine learning models all work together.
This combination enables the phone to extract the signal most likely to contain the user's speech from among the sounds of cars, music, conversations, and other ambient noises, and then provide it to the speech recognition system.
Of course, these technologies are not flawless, and very noisy environments, multiple simultaneous speakers, large distance from the phone, or sudden sounds can still reduce the system's accuracy. Nevertheless, advances in audio processing and AI models have enabled modern phones to recognize human speech with remarkable accuracy in conditions that would be very difficult for a single microphone.