Audio analytics analyze and extract information from unstructured audio data. When applied to human spoken language, audio analytics is also referred to as speech analytics. Since these techniques have mostly been applied to spoken audio, the terms audio analytics and speech analytics are often used interchangeably. Currently, customer call centers and healthcare are the primary application areas of audio analytics. Call centers use audio analytics for efficient analysis of thousands or even millions of hours of recorded calls. These techniques help improve customer experience, evaluate agent performance, enhance sales turnover rates, monitor compliance with different policies (e.g., privacy and security policies), gain insight into customer behavior, and identify product or service issues, among many other tasks. Audio analytics systems can be designed to analyze a live call, formulate cross/up-selling recommendations based on the customer’s past and present interactions, and provide feedback to agents in real time. In addition, automated call centers use the Interactive Voice Response (IVR) platforms to identify and handle frustrated callers. In healthcare, audio analytics support diagnosis and treatment of certain medical conditions that affect the patient’s communication patterns (e.g., depression, schizophrenia, and cancer). Also, audio analytics can help analyze an infant’s cries, which contain information about the infant’s health and emotional status. The vast amount of data recorded through speech-driven clinical documentation systems is another driver for the adoption of audio analytics in healthcare. Speech analytics follows two common technological approaches: the transcript-based approach (widely known as large-vocabulary continuous speech recognition, LVCSR) and the phonetic-based approach.