BERSting at the Screams: 一个面向远距离、情绪化及呼喊语音识别的基准
BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech Recognition
AI总结：
针对现有语音识别系统在远距离、情绪化呼喊等真实场景表现不佳的问题，本文提出包含多口音、多情绪、多采集位置的BERSt数据集，提供ASR与SER初始基准，验证其挑战性以推动系统鲁棒性提升。
摘要：
部分语音识别任务（如自动语音识别，即ASR）在诸多已报告的指标上正接近甚至达到人类水平，但在复杂的真实场景（例如远距离语音场景）中仍表现不佳。此前的挑战赛已发布数据集以解决远距离ASR问题，但研究重点仍主要围绕距离展开，且专门依赖多麦克风阵列系统。\n本文提出了B(asic) E(motion) R(andom phrase) S(hou)t(s)（BERSt）数据集。该数据集包含来自98名演员的近4小时英语语音，这些演员带有不同的地域口音和非母语口音。数据在演员家中通过智能手机采集，因此涵盖至少98种不同的声学环境。数据还包含7种不同的情绪提示，以及呼喊和口语两种发声方式。智能手机被放置在19个不同位置，包括有遮挡物的位置以及与演员不在同一房间的位置。\n该数据集公开可用，可用于评估多种语音识别任务，包括：ASR、呼喊检测以及语音情感识别（SER）。我们为ASR和SER任务提供了初始基准，结果发现ASR性能会随着距离增加和呼喊程度提升而下降，且性能表现会因目标情绪的不同而存在差异。我们的结果表明，BERSt数据集对ASR和SER任务均具有挑战性，需要持续开展工作以提升此类系统的鲁棒性，从而实现更精准的真实场景应用。
英文摘要：
Some speech recognition tasks, such as automatic speech recognition (ASR), are approaching or have reached human performance in many reported metrics. Yet, they continue to struggle in complex, real-world, situations, such as with distanced speech. Previous challenges have released datasets to address the issue of distanced ASR, however, the focus remains primarily on distance, specifically relying on multi-microphone array systems. Here we present the B(asic) E(motion) R(andom phrase) S(hou)t(s) (BERSt) dataset. The dataset contains almost 4 hours of English speech from 98 actors with varying regional and non-native accents. The data was collected on smartphones in the actors homes and therefore includes at least 98 different acoustic environments. The data also includes 7 different emotion prompts and both shouted and spoken utterances. The smartphones were places in 19 different positions, including obstructions and being in a different room than the actor. This data is publicly available for use and can be used to evaluate a variety of speech recognition tasks, including: ASR, shout detection, and speech emotion recognition (SER). We provide initial benchmarks for ASR and SER tasks, and find that ASR degrades both with an increase in distance and shout level and shows varied performance depending on the intended emotion. Our results show that the BERSt dataset is challenging for both ASR and SER tasks and continued work is needed to improve the robustness of such systems for more accurate real-world use.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
