From Klingon to Simlish: Deepgram's AI Language Detection Adventures

The text discusses the process of AI language detection in automated speech recognition (ASR) applications. It explains how machine learning is used to solve a classification problem where the objective is to accurately identify the label or language of a given text or audio sample. Features such as Mel-frequency cepstral coefficients (MFCCs) are extracted from training data and passed to a model for training, which learns to identify underlying patterns and correlations between the features and corresponding language labels. The Deepgram API is used to perform language detection on audio samples, and the text demonstrates how this works in real-time using conlangs or constructed languages. The author also provides code examples of how to use the Deepgram Python SDK for language detection.