利用语音基础模型中的语言选择性维度进行语码转换检测
Code-Switching Detection via Language-Selective Dimensions in Speech Foundation Models
研究者提出一种轻量方法，直接从语音中检测语码转换（CS），无需依赖多语言转录。该方法利用预训练语音编码器的帧级语言信息，通过单语录音识别语言选择性隐藏状态维度并聚合其帧级激活进行预测。在 CS-FLEURS 的 14 个语言对上，Whisper-small 编码器平均 F1 达 0.798，高于 CNN 的 0.776 和 HuBERT 线性基线的 0.751。
AuthorsJie Chi, Zakaria Aldeneh, Masha Fedzechkina, Maureen de Seyssel, Natalie Schluter
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 encoders to determine whether both languages are present within an utterance. Specifically, we identify language-selective hidden-state dimensions using monolingual recordings and aggregate their frame-level activations into a CS prediction. With a small CS set used to tune a decision rule, we show that the Whisper-small encoder achieves a mean F1 of 0.798 across 14 language pairs in CS-FLEURS, compared with 0.776 for a CNN and 0.751 for a HuBERT linear baseline trained for monolingual language identification. Our findings suggest that pretrained speech encoders already encode the information needed to identify CS utterances, and they provide a lightweight tool for building more representative multilingual corpora.
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来源：Apple Machine Learning Research · machinelearning.apple.com