REVIEW 4 cited by
End-to-end Continuous Speech Recognition using Attention-based Recurrent NN: First Results
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
read the original abstract
We replace the Hidden Markov Model (HMM) which is traditionally used in in continuous speech recognition with a bi-directional recurrent neural network encoder coupled to a recurrent neural network decoder that directly emits a stream of phonemes. The alignment between the input and output sequences is established using an attention mechanism: the decoder emits each symbol based on a context created with a subset of input symbols elected by the attention mechanism. We report initial results demonstrating that this new approach achieves phoneme error rates that are comparable to the state-of-the-art HMM-based decoders, on the TIMIT dataset.
Forward citations
Cited by 4 Pith papers
-
Improving Practical Aspects of End-to-End Multi-Talker Speech Recognition for Online and Offline Scenarios
Combining single-channel speech separation with end-to-end multi-talker ASR improves accuracy on heavily overlapped audio, and a new segment-based output ordering aids offline transcription readability.
-
Aligner-Encoders: Self-Attention Transformers Can Be Self-Transducers
A transformer speech encoder can learn to internally rearrange audio information into text order, enabling a lightweight decoder trained with simple cross-entropy to nearly match RNN-Transducer accuracy with faster inference.
-
Cross-Attention End-to-End ASR for Two-Party Conversations
End-to-end ASR model with speaker-specific cross-attention for two-party conversations outperforms standard models on the Switchboard corpus.
-
End-to-End ASR for Code-switched Hindi-English Speech
End-to-end ASR for code-switched Hindi-English with <50 hours of data shows gains from multi-task learning and corpus balancing but underperforms cascaded baselines.
Discussion (0). Continue with ORCID to comment.