Due to individual differences in physical attributes like vocal tract shapes, every person possesses a distinct voice pattern. In automatic speech recognition (ASR), this uniqueness is harnessed to identify and analyze speakers by extracting and analyzing voice features such as pitch and frequencies.
So, how exactly can AI-powered technologies be utilized for this purpose? This is where speaker recognition comes into play.
If you're building an audio or video-based product, this is a must-read to understand the mechanics of ASR speaker recognition, learn about the differences between identification, verification and speaker diarization, and get an overview of the key factors to consider when building ASR speakers detection systems.
Speaker Recognition (SR) is an old and critical field of AI, focused on solving the problems of speaker identification, verification and diarization.
Before diving into how each of these works, the different use cases they unlock, and how to choose the most suitable multi-speaker system for your needs, let's start by looking at the most fundamental element in the field of speech recognition – sound.
Sound is a wave vibration that travels through air until it reaches the listener's ear and makes us understand what is being said.
The sound in the air is a continuous signal that can be represented in time-domain; it shows the amplitude change of the signal as a function of time.
When it comes to digital audio, the sound is converted into a discrete signal that can be represented in frequency-domain; it shows the amplitude change of the signal over different frequencies.
For AI-powered speech recognition systems, every person’s voice represents a unique sound to be deciphered. The key properties of the multi-speaker input audio that are taken into account for speaker recognition include wavelength, frequency, pitch, amplitude and sample rate.
Wavelength refers to the physical length of a sound wave and is measured from one peak of the wave to the following one. Analyzing the wavelength serves to identify unique patterns in speech, as different individuals produce speech with variations in wavelength due to their vocal tract differences.
Frequency is the rate at which a sound wave vibrates. It's calculated by the number of complete wave cycles in a second, and is measured in Hertz (Hz). Frequency patterns are used to differentiate between speakers, based on individual differences in vocal ranges and speech patterns.
Pitch is an attribute of sound, which influences how we perceive the highness or lowness of a sound. It's used to differentiate if the tone is strident or flat. Based on the wave's frequency, one can know the quality of sound.
Amplitude refers to the magnitude or strength of a sound wave. It represents the height of the wave and shows how strong it is. While not used by default for speaker recognition, it can be relevant for normalizing speech signals to ensure consistent analysis across different recordings.
Measured in Hz, sample rate refers to the number of the samples of audio carried per second. Each sample consists of several conversions of small parts of the signal. It generates an array that is used as a digital representation of the signal.
Sample rate plays a key role in ASR speaker recognition, helping to capture detailed information about the speaker’s particular pronunciation, intonation, and other speaker-specific features.
For best results, it’s better to have a higher sample rate to capture more nuances. This is done by minimizing the distance between samples, providing a closer representation of the original wave. Bear in mind that, while favourable for speaker recognition, higher sample rate requires additional resources.
There are minimal frequencies under which the sound is intelligible for ASR models. Most audio files being produced today are at a minimum of 40 kHz, but some types of audio – such as phone recordings from call centers – are at lower frequencies, resulting in recordings at 16 kHz or even 8 kHz. Higher frequencies, such as mp3 files at 128 kHz, need to be resampled.
The elements of sound theory, including wavelength, frequency, pitch, and sample rate, are essential prerequisites for speaker recognition models to extract unique characteristics of voice.
By analyzing patterns in wavelength, frequency distribution, pitch variations, and even considering aspects like amplitude and sample rate for signal quality, these systems can differentiate between different speakers and improve accuracy in speaker recognition tasks.
There are two approaches used in the speaker recognition field, specifically for identification , to carry out this process.
Audio fingerprinting is a classical method of speaker recognition based on extracting a distinctive identifier, or fingerprint, from the audio signal. This technique utilizes Fourier transform to generate a spectrogram from the audio file, enabling comparison with the existing database. Specifically, it zeroes in on unique features of the speaker's voice, particularly in ASR speaker detection tasks.
One of the most well-known alternative techniques to audio fingerprinting involves training a machine learning (ML) model on a diverse dataset encompassing various speakers and speech lengths.
This trained model can then be employed to authenticate speaker identity in unfamiliar audio files that were not part of its training data. This approach provides the most relevant result using a scoring technique. To delve deeper into the ASR models and how they function, explore our introduction to speech-to-text AI.
Note that the two approaches are not mutually exclusive: they can work simultaneously or by combining them together. For example, we can use the audio fingerprinting approach for speaker identification and the ML approach for speaker verification, or for robustness and speed in identification, we can use fingerprinting while using ML to improve the accuracy over time.
Speaker identification is the process of identifying which of the known speakers have spoken. Unlike ASR which focuses on spoken speech, this process usually is text independent, meaning that we don't focus on what is being said, but how it's being said. This can be done in two ways describe above: either by getting a spectrogram from the audio and comparing it with what we have in the database, or, leveraging ML to extract features from the audio and using trained models to predict the speaker with the most similar voice.
Example of use cases: Access control, forensic investigations
Speaker verification is the process of verifying the speaker identity and ensuring that he/she is the one who should be. The process of verification could be text-dependent or independent. It's done by extracting speaker embeddings from the audio and then comparing it with the reference one and get the result based on similarity score.
Example of use cases: Security and fraud
In audio transcription, speaker diarization is a process of automatically partitioning an audio recording into segments that correspond to different speakers. This is done by using a combination of techniques to distinguish and cluster segments of an audio signal based on speaker embeddings, producing a transcript with multiple speakers.
For a deep dive information about the inner workings of diarization, check out our blog.
Example of use cases: Conferences, media monitoring, security identification.
While ASR systems are becoming more robust and better at recognizing speakers, the modern business environment presents an additional challenge – the fact that communication occurs in multiple languages.
Podcasts, meetings and live events can have a lot of speakers with different languages and dialects. It may even happen that the same speaker switches from one language to the next in the course of the same audio recording – but is that a task all ASR systems are fit to solve?
Thanks to the latest advances in ASR, speaker recognition mechanisms have evolved significantly from acoustic-based and/or fingerprinting recognition of speakers to a sophisticated ML-driven approach, based on embeddings that contain nuanced speaker information, traceable across recordings.
That said, certain factors needs to be taken into account when integrating speech recognition capabilities into an ASR product, be it through an open-source platform or an API route.
1- Acoustic challenges: Different languages possess distinct acoustic properties crucial for speaker identification. What works well for one language might not be as effective for another.
2- Linguistic challenges: Speakers exhibit diverse speech patterns based on their language. Some may speak more rapidly than others due to both personal specificities as well as inherent language traits: for example, Japanese was found to be the fastest spoken language in the world, while Mandarin - the slowest, based on the number of syllables spoken per second/minute. This presents an obvious challenge for accurate recognition - especially in an audio where the same speaker intervenes in multiple languages.
3- Audio quality: Whatever the language, high-quality input audio gives better results than audio recorded in noisy environments like call centers or emergency services. To mitigate this, special model optimization techniques, akin the one deployed by Gladia to optimize Whisper ASR, are needed.
To properly address these challenges, the following features are a must-have to achieve the best performance:
By integrating robust multi-speaker transcription and advanced language support into your product, you can unleash a wealth of features that empower users to maximize their experience during meetings, conversations, and live events.
At Gladia, we provide a plug-and-play audio intelligence API, including state-of-the-art diarization, live transcription, translation and code-switching among other features, enabling virtual meeting, media and call center companies to harness the benefits of AI for voice. Feel free to learn more about our product and pricing, or reach out to us directly for a demo.
As we discussed in this article, speaker recognition encompasses is a powerful set of AI technologies, used for a variety of crucial identity-based task. With speaker recognition and ASR combined, companies can benefit from additional insights into user behaviour and leverage this data for better analysis, improved customer support and more informed decision-making.
Article written for Gladia by Ahmed Esawy.
At Gladia, we built an optimized version of Whisper in the form of an API, adapted to real-life professional use cases and distinguished by exceptional accuracy, speed, extended multilingual capabilities, and state-of-the-art features, including speaker diarization and word-level timestamps.
Speech-To-Text
Large Language Models (LLMs) are at the forefront of the boom in artificial intelligence (AI) and they continue to get more advanced. However, LLMs can suffer from performance issues, and produce inaccurate, misleading, or biased information, leading to poor user experience and creating difficulties for product builders.
Speech-To-Text
Over the last few years, Large Language Models (LLMs) have become accessible and transformative tools, powering everything from customer support and content generation to complex, industry-specific applications in healthcare, education, and finance.
Case Studies
In recent years, fuelled by advancements in LLMs, the numbers of AI note-takers has skyrocketed. These apps are increasingly tailored to meet the unique needs of specific user groups, such as doctors, sales teams and project managers.
