Speech technologies enable computing devices to understand or generate speech data. It resides in an intersection amongst natural language processing, signal processing, and machine learning. We use speech technology in our day-to-day life; such as human to machine communication; when we talk to our smart devices or even human to human like speech to speech translation. Speech technologies cover a wide range of applications such as speech enhancement, speaker recognition, speech analysis..etc. However, in ArabiSpeech, we focus on challenges that depend on the Arabic language. primarily, we focus on Arabic Automatic Speech Recognition, Arabic Dialect Identification, Arabic Text to Speech, and Arabic Text to Speech.
Arabic Dialect Identification
Dialect identification (DID) is a special case of Language identification (LID) problem. This is the process of automatically identifying the language class for a given speech segment
Language Models are building block of many speech and NLP technology that aims at accurately estimating the probability distribution of a word sequences or sentences produced in a natural language