Multilingual Automatic Speech Recognition (ASR) models are capable of
transcribing audios across multiple languages, eliminating the need for
separate models. In addition, they can perform Language Identification (LID)
and handle code-switched speech. However, training these models requires
special code-switch and multilingual speech corpora which are sparsely
available. In this paper, we evaluate different approaches towards training of
bilingual as well as code-switched ASR models using purely monolingual data
sources. We introduce the concept of aggregate tokenizers that differs from the
current prevalent technique of generating LIDs at the boundaries of monolingual
samples and produces LID for each emitted token instead. We compare bilingual
and monolingual model performance, showcase the efficacy of aggregate
tokenizers, present a synthetic code-switched ASR data generation technique and
demonstrate the effectiveness of the proposed code-switched ASR models for the
tasks of speech recognition and spoken language identification.

本文介绍了一种新的聚合分词器方法，用于训练双语和混合语音自动语音识别模型，从而消除了获得语料库的限制，并演示了这种方法的有效性。