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Efficient Sequence Transduction by Jointly Predicting Tokens and Durations

ICML 2023 · Architectures and efficiency › Attention and transformers · approach: State space and recurrent sequence models · 6 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Add joint token-and-duration prediction with durations up to 8Conformer-Large transducer · Librispeech test-cleanWER (%): 2.14 -> 2.11Helped
Add joint token-and-duration prediction with durations up to 8Conformer-Large transducer · Librispeech test-cleantime (s): 256 -> 117Helped
Add joint token-and-duration prediction with durations up to 8Conformer-Large transducer · Librispeech test-otherWER (%): 5.11 -> 5.16No clear effect
Add joint token-and-duration prediction with durations up to 8Conformer-Large transducer · Librispeech test-othertime (s): 244 -> 115Helped
Add joint token-and-duration prediction with durations up to 8Conformer-Large transducer · Spanish CallhomeWER (%): 19.84 -> 18.73Helped
Add joint token-and-duration prediction with durations up to 8Conformer-Large transducer · German MLS testtime (s): 558 -> 198Helped
Add joint token-and-duration prediction with durations up to 8Conformer-Large transducer · MUST-C V2 TestBLEU (%): 23.21 -> 24.47Helped
Add joint token-and-duration prediction with durations up to 8Conformer-Large transducer · MUST-C V2 Testtime (s): 218 -> 96Helped
Add joint token-and-duration prediction with durations up to 8Conformer-Large transducer · SLURPintent accuracy: 88.53 -> 90.07Helped
Add joint token-and-duration prediction with durations up to 8Conformer-Large transducer · TTS repeated digitsWER%: 64.62 -> 5.78Helped
Train with sampled conventional transducer loss at probability ω = 0.1TDT ASR · Librispeech test-clean, batch=4WER (%): RNNT 2.13; TDT 0-2 2.10; TDT 0-4 2.15; TDT 0-6 2.10; TDT 0-8 2.13Mixed
Increase the maximum supported duration from 2 to 8 (ablation)TDT · TTS repeated digitsWER%: 12.59 -> 9.35 -> 6.12 -> 5.78Helped

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