# BERSting at the Screams: A Benchmark for Distanced, Emotional and Shouted Speech Recognition arXiv: https://arxiv.org/abs/2505.00059 ยท HTML: https://mlsanity.com/paper/2505.00059/ ## Key facts - Cited by 5 papers in the map (data through 14 Oct 2026). - Builds on 38 of its 99 references that are in the map. - First posted on arXiv 30 Apr 2025. ## Builds on (most cited first, 30 of 38) - [wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations](https://mlsanity.com/paper/2006.11477/) (2020) - [Common Voice: A Massively-Multilingual Speech Corpus](https://mlsanity.com/paper/1912.06670/) (2019) - [SUPERB: Speech processing Universal PERformance Benchmark](https://mlsanity.com/paper/2105.01051/) (2021) - [VoxPopuli: A Large-Scale Multilingual Speech Corpus for Representation Learning, Semi-Supervised Learning and Interpretation](https://mlsanity.com/paper/2101.00390/) (2021) - [SpeechBrain: A General-Purpose Speech Toolkit](https://mlsanity.com/paper/2106.04624/) (2021) - [GigaSpeech: An Evolving, Multi-domain ASR Corpus with 10,000 Hours of Transcribed Audio](https://mlsanity.com/paper/2106.06909/) (2021) - [AISHELL-1: An Open-Source Mandarin Speech Corpus and A Speech Recognition Baseline](https://mlsanity.com/paper/1709.05522/) (2017) - [TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptation](https://mlsanity.com/paper/1805.04699/) (2018) - [Dawn of the transformer era in speech emotion recognition: closing the valence gap](https://mlsanity.com/paper/2203.07378/) (2022) - [CHiME-6 Challenge:Tackling Multispeaker Speech Recognition for Unsegmented Recordings](https://mlsanity.com/paper/2004.09249/) (2020) - [Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition](https://mlsanity.com/paper/2305.05084/) (2023) - [QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions](https://mlsanity.com/paper/1910.10261/) (2019) - [The fifth 'CHiME' Speech Separation and Recognition Challenge: Dataset, task and baselines](https://mlsanity.com/paper/1803.10609/) (2018) - [A Fine-tuned Wav2vec 2.0/HuBERT Benchmark For Speech Emotion Recognition, Speaker Verification and Spoken Language Understanding](https://mlsanity.com/paper/2111.02735/) (2021) - [SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition](https://mlsanity.com/paper/2104.02014/) (2021) - [AudioMNIST: Exploring Explainable Artificial Intelligence for Audio Analysis on a Simple Benchmark](https://mlsanity.com/paper/1807.03418/) (2018) - [AfriSpeech-200: Pan-African Accented Speech Dataset for Clinical and General Domain ASR](https://mlsanity.com/paper/2310.00274/) (2023) - [Far-Field Automatic Speech Recognition](https://mlsanity.com/paper/2009.09395/) (2020) - [Voices Obscured in Complex Environmental Settings (VOICES) corpus](https://mlsanity.com/paper/1804.05053/) (2018) - [The CHiME-7 DASR Challenge: Distant Meeting Transcription with Multiple Devices in Diverse Scenarios](https://mlsanity.com/paper/2306.13734/) (2023) - [Fusing ASR Outputs in Joint Training for Speech Emotion Recognition](https://mlsanity.com/paper/2110.15684/) (2021) - [Vistaar: Diverse Benchmarks and Training Sets for Indian Language ASR](https://mlsanity.com/paper/2305.15386/) (2023) - [Highway Long Short-Term Memory RNNs for Distant Speech Recognition](https://mlsanity.com/paper/1510.08983/) (2015) - [Building state-of-the-art distant speech recognition using the CHiME-4 challenge with a setup of speech enhancement baseline](https://mlsanity.com/paper/1803.10109/) (2018) - [MediaSpeech: Multilanguage ASR Benchmark and Dataset](https://mlsanity.com/paper/2103.16193/) (2021) - [The VOiCES from a Distance Challenge 2019 Evaluation Plan](https://mlsanity.com/paper/1902.10828/) (2019) - [The CHiME-8 DASR Challenge for Generalizable and Array Agnostic Distant Automatic Speech Recognition and Diarization](https://mlsanity.com/paper/2407.16447/) (2024) - [ASR and Emotional Speech: A Word-Level Investigation of the Mutual Impact of Speech and Emotion Recognition](https://mlsanity.com/paper/2305.16065/) (2023) - [Exploration of End-to-End ASR for OpenSTT -- Russian Open Speech-to-Text Dataset](https://mlsanity.com/paper/2006.08274/) (2020) - [Neural Blind Source Separation and Diarization for Distant Speech Recognition](https://mlsanity.com/paper/2406.08396/) (2024) ## Built on by (most cited first, 5 of 5) - [Mega-ASR: Towards In-the-wild^2 Speech Recognition via Scaling up Real-world Acoustic Simulation](https://mlsanity.com/paper/2605.19833/) (2026) - [EmoNet-Voice: A Fine-Grained, Expert-Verified Benchmark for Speech Emotion Detection](https://mlsanity.com/paper/2506.09827/) (2025) - [RW-Voice-EQ Bench: A Real World Benchmark for Evaluating Voice AI Systems](https://mlsanity.com/paper/2607.14846/) (2026) - [Synthesizing the Lombard Effect: Multi-Level Control of Speech Clarity and Vocal Effort in TTS](https://mlsanity.com/paper/2606.23176/) (2026) - [VoiceNet: Fine-Grained Voice Understanding Beyond Emotion at Scale](https://mlsanity.com/paper/2609.32016/) (2026) ## Abstract (arXiv) 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.