In this paper, we describe several techniques for improving the acoustic and
language model of an automatic speech recognition (ASR) system operating on
code-switching (CS) speech. We focus on the recognition of Frisian-Dutch radio
broadcasts where one of the mixed languages, namely Frisian, is an
under-resourced language. In previous work, we have proposed several automatic
transcription strategies for CS speech to increase the amount of available
training speech data. In this work, we explore how the acoustic modeling (AM)
can benefit from monolingual speech data belonging to the high-resourced mixed
language. For this purpose, we train state-of-the-art AMs, which were
ineffective due to lack of training data, on a significantly increased amount
of CS speech and monolingual Dutch speech. Moreover, we improve the language
model (LM) by creating code-switching text, which is in practice almost
non-existent, by (1) generating text using recurrent LMs trained on the
transcriptions of the training CS speech data, (2) adding the transcriptions of
the automatically transcribed CS speech data and (3) translating Dutch text
extracted from the transcriptions of a large Dutch speech corpora. We report
significantly improved CS ASR performance due to the increase in the acoustic
and textual training data.

本文提出了改进自动语音识别系统的语音和语言模型技术，用于混合语言 Frision 和 Dutch 广播语音的语音识别，通过训练包括单语及混合码的语音数据集，改善了模型效果并提高了混合语音自动识别性能。