Multi-Dialect Arabic Speech Recognition

TL;DR

This paper develops a multi-dialect Arabic speech recognition system using deep neural networks, creating a large normalized corpus and achieving state-of-the-art accuracy with a convolutional-recurrent architecture.

Contribution

It introduces a large multi-dialect Arabic speech corpus and a novel deep learning framework that outperforms previous systems in accuracy.

Findings

Achieved a 14% error rate in speech recognition.

Developed a normalized, multi-dialect Arabic speech corpus.

Implemented a convolutional-recurrent neural network architecture.

Abstract

This paper presents the design and development of multi-dialect automatic speech recognition for Arabic. Deep neural networks are becoming an effective tool to solve sequential data problems, particularly, adopting an end-to-end training of the system. Arabic speech recognition is a complex task because of the existence of multiple dialects, non-availability of large corpora, and missing vocalization. Thus, the first contribution of this work is the development of a large multi-dialectal corpus with either full or at least partially vocalized transcription. Additionally, the open-source corpus has been gathered from multiple sources that bring non-standard Arabic alphabets in transcription which are normalized by defining a common character-set. The second contribution is the development of a framework to train an acoustic model achieving state-of-the-art performance. The network architecture comprises of a combination of convolutional and recurrent layers. The spectrogram features of the audio data are extracted in the frequency vs time domain and fed in the network. The output frames, produced by the recurrent model, are further trained to align the audio features with its corresponding transcription sequences. The sequence alignment is performed using a beam search decoder with a tetra-gram language model. The proposed system achieved a 14% error rate which outperforms previous systems.