AppTek Call Center Dialogues: A Multi Accent Long Form Benchmark for English ASR AppTek Call Center Dialogues is a long form conversational speech dataset for automatic speech recognition (ASR), featuring diverse English accents across multiple service oriented domains and designed to evaluate models on realistic call center interactions . 128.6 hours of speech 14 English accent groups 16 service domains 5–15 minute conversations (long form) Split channel audio (one speaker per file) Unlike common ASR benchmarks (e.g., LibriSpeech, Common Voice), this dataset emphasizes: spontaneous conversational speech accent diversity and robustness segmentation sensitive evaluation To our knowledge, this is the largest publicly available dataset of English accented conversational speech collected under controlled and comparable conditions. Quickstart Recommended open source segmentation: Silero VAD ( silero vad==5.1.2 ) min silence: 10.0 s, min speech: 0.25 s, max speech: 30 s Evaluation: Whisper normalization ( openai whisper 20250625 ), dataset specific normalization, WER via jiwer Load Dataset Dataset Details Dataset Description AppTek Call Center Dialogues is a long form English ASR benchma…
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