Automatic speaker recognition (ASR) leverages the unique vocal patterns of individuals to identify and analyze speakers by examining voice features such as pitch and frequency. This technology is crucial for audio and video-based products, enabling tasks like speaker identification, verification, and diarization. Speaker recognition involves understanding the fundamental elements of sound, such as wavelength, frequency, pitch, amplitude, and sample rate, to differentiate between speakers. Two primary methods are used: audio fingerprinting, which quickly compares audio spectrograms with a database, and machine learning, which trains models on diverse datasets for more accurate results. These approaches can be combined to enhance accuracy and efficiency. The integration of ASR systems into products must consider multilingual environments, acoustic and linguistic challenges, and audio quality to ensure effective speaker recognition, identification, and transcription. Companies like Gladia offer APIs that incorporate advanced features such as live transcription, translation, and code-switching, empowering users to maximize their experiences during meetings and events while providing insights into user behavior for improved decision-making.