Towards End-To-End Speech Recognition with Recurrent Neural Networks
Why is this work in the frame?
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Machine scores (provisional)
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
- Teacher spread
- 0.215 · how far apart the two teachers sit on this one work
- Validation status
score_only:v0-immature-baseline· verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it
Abstract
This paper presents a speech recognition sys-tem that directly transcribes audio data with text, without requiring an intermediate phonetic repre-sentation. The system is based on a combination of the deep bidirectional LSTM recurrent neural network architecture and the Connectionist Tem-poral Classification objective function. A mod-ification to the objective function is introduced that trains the network to minimise the expec-tation of an arbitrary transcription loss function. This allows a direct optimisation of the word er-ror rate, even in the absence of a lexicon or lan-guage model. The system achieves a word error rate of 27.3 % on the Wall Street Journal corpus with no prior linguistic information, 21.9 % with only a lexicon of allowed words, and 8.2 % with a trigram language model. Combining the network with a baseline system further reduces the error rate to 6.7%. 1.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
The record
- Venue
- Topic
- Speech Recognition and Synthesis
- Field
- Computer Science
- Canadian institutions
- University of Toronto
- Funders
- —
- Keywords
- Computer scienceWord error rateTrigramSpeech recognitionConnectionismLanguage modelRecurrent neural networkArtificial intelligenceLexiconArtificial neural networkWord (group theory)Time delay neural networkNatural language processing
- Has abstract in OpenAlex
- yes