Closed-set spoken language identification is the task of recognizing the
language being spoken in a recorded audio clip from a set of known languages.
In this study, a language identification system was built and trained to
distinguish between Arabic, Spanish, French, and Turkish based on nothing more
than recorded speech. A pre-existing multilingual dataset was used to train a
series of acoustic models based on the Tedlium TDNN model to perform automatic
speech recognition. The system was provided with a custom multilingual language
model and a specialized pronunciation lexicon with language names prepended to
phones. The trained model was used to generate phone alignments to test data
from all four languages, and languages were predicted based on a voting scheme
choosing the most common language prepend in an utterance. Accuracy was
measured by comparing predicted languages to known languages, and was
determined to be very high in identifying Spanish and Arabic, and somewhat
lower in identifying Turkish and French.

本研究构建并训练了一种基于自动语音识别技术的语言识别系统，可自动识别阿拉伯语、西班牙语、法语和土耳其语，并通过投票方式进行预测，准确率很高。