Tunisian Dialectal End-to-end Speech Recognition based on DeepSpeech
Abir Messaoudi, Hatem Haddad, Chayma Fourati, Moez Ben HajHmida, Aymen Ben Elhaj Mabrouk, Mohamed Graïet · Procedia Computer Science · 2021
Recognize automatically the spontaneous Human speech and transcribe it into text is becoming an important task. However, freely available models are rare especially for under-resourced languages and dialects since they require large amounts of data in order to achieve high performances. This paper describes an approach to build an end-to-end Tunisian dialect speech system based on deep learning. For this propose, a Tunisian dialect paired text-speech dataset called "TunSpeech" was created. Existing Modern Standard Arabic (MSA) speech data was also combined with dialectal Tunisian data and decreased the Out-Of-Vocabulary rate and improve perplexity. On the other hand, synthetic dialectal data from a text to speech increased the Word Error Rate.