CLIASR: A Combined Automatic Speech Recognition and Language Identification System

In this paper, we aim to describe a novel technique which is a Combined Automatic Speech Recognition and Language Identification System that uses both ASR and LI technologies which consists of the recognition of spoken digits after identifying their language. An in-house corpus was used mainly for both speech-based multi-lingual identification and speech recognition tasks made of bilingual digits sounds that contains ten digits spoken mainly in two languages: Modern Standard Arabic (MSA) and Amazigh Moroccan dialect. First of all, we develop the Language Identification stage which is the basis of our hybrid system which behaves as front-end of our system that serves for spoken language detection. This facilitates the task of recognition by allocating the output to the appropriate Hidden Model Markov (HMM) based recognition system (Arabic or Amazigh) which improves recognition of a bilingual spoken digit efficiently. For this purpose, a set of parameters were adjusted on our CLIASR system to achieve good results, including classifier parameters, feature vector, and HMM-GMM parameters. The results show that our proposed LI-ASR system performs 33% better than an ordinary ASR for a given bilingual mixed speech corpus.