Speech Technologies
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.
Automatic Speech Recognition
Automatic Speech Recognition (ASR) is the process of converting the speech signal into its corresponding text. The quality of ASR systems is measured by how close their recognized sequences of words are to human recognized sequences of words.
Arabic Dialect Identification
Automatically identifying the input dialect from the speech signal. There are four major dialects for Arabic, including Egyptian, Gulf, Levantine and North African in addition to modern standard Arabic (MSA)
Language Models
Language Modeling aims at accurately estimating the probability distribution of word sequences or sentences produced in a natural language such as Arabic
Text to Speech
The Text to Speech (TTS) technology aims to convert a sequence of words into speech