Apple 研究：用语音基础模型的语言选择维度直接检测语码转换
Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models
Apple 出了个轻量方法，直接从音频判断一句话里有没有切换语言，省掉先转写再判断的那步。
Apple 机器学习团队发布语码转换（code-switching）检测研究，目标是在大规模语音集合中找出含语言切换的语句，用于补充训练数据。语码转换指同一句话内在不同语言之间切换，这类语音在预训练语料中的覆盖不足。已有做法需要先得到准确的多语言转写，再从文本里检测语言交替；Apple 提出的方法绕开转写环节，直接从音频判断。方法利用预训练语音基础模型中的帧级语言信息和语言选择性维度，实现轻量的语音侧检测。
Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models
Code-switching (CS), the alternation between languages within an utterance, is common in multilingual speech but underrepresented in pretraining corpora. Increasing CS coverage requires finding relevant utterances in large speech collections so that they can be included in training data. One way to find such utterances is to transcribe the speech and detect language alternation in the text, but this depends on obtaining accurate multilingual transcriptions. We propose a lightweight method that detects CS directly from speech. It uses frame-level language information in pretrained speech…
