X2-Turn: Frame-Synchronous Dual-Head Modeling for Joint Streaming ASR and Turn State Prediction
Researchers have developed a new method for spoken dialogue systems to predict when a user has finished speaking. The approach, called X2-Turn, uses a single model that jointly predicts the words being spoken and the turn state (i.e., whether it's time for the system to respond). This is an improvement over previous methods, which often relied on separate models or optimized for fixed chunks of speech. In experiments, X2-Turn achieved accurate turn-taking detection while keep
Researchers have developed a new method for spoken dialogue systems to predict when a user has finished speaking. The approach, called X2-Turn, uses a single model that jointly predicts the words being spoken and the turn state (i.e., whether it's time for the system to respond). This is an improvement over previous methods, which often relied on separate models or optimized for fixed chunks of speech. In experiments, X2-Turn achieved accurate turn-taking detection while keeping processing times low.
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Why it matters: This matters because spoken dialogue systems need to quickly and accurately determine when a user has finished speaking in order to respond appropriately. Improving this aspect can lead to more natural and efficient human-computer interactions.
Source: https://arxiv.org/abs/2608.10878
This article was originally published at: https://arxiv.org/abs/2608.10878