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Found 9 papers, 0 papers with code
External Language Model Integration for Factorized Neural Transducers
We propose an adaptation method for factorized neural transducers (FNT) with external language models.
Streaming Punctuation: A Novel Punctuation Technique Leveraging Bidirectional Context for Continuous Speech Recognition
While speech recognition Word Error Rate (WER) has reached human parity for English, continuous speech recognition scenarios such as voice typing and meeting transcriptions still suffer from segmentation and punctuation problems, resulting from irregular pausing patterns or slow speakers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
TRScore: A Novel GPT-based Readability Scorer for ASR Segmentation and Punctuation model evaluation and selection
Punctuation and Segmentation are key to readability in Automatic Speech Recognition (ASR), often evaluated using F1 scores that require high-quality human transcripts and do not reflect readability well.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Smart Speech Segmentation using Acousto-Linguistic Features with look-ahead
For the downstream task of machine translation, it improves the translation BLEU score by an average of 1. 05 points.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+5
Four-in-One: A Joint Approach to Inverse Text Normalization, Punctuation, Capitalization, and Disfluency for Automatic Speech Recognition
Features such as punctuation, capitalization, and formatting of entities are important for readability, understanding, and natural language processing tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Streaming Punctuation for Long-form Dictation with Transformers
While speech recognition Word Error Rate (WER) has reached human parity for English, long-form dictation scenarios still suffer from segmentation and punctuation problems resulting from irregular pausing patterns or slow speakers.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Multilingual Transformer Language Model for Speech Recognition in Low-resource Languages
It is challenging to train and deploy Transformer LMs for hybrid speech recognition 2nd pass re-ranking in low-resource languages due to (1) data scarcity in low-resource languages, (2) expensive computing costs for training and refreshing 100+ monolingual models, and (3) hosting inefficiency considering sparse traffic.
LSTM-LM with Long-Term History for First-Pass Decoding in Conversational Speech Recognition
The context information is captured by the hidden states of LSTM-LMs across utterance and can be used to guide the first-pass search effectively.
Long-span language modeling for speech recognition
We conduct language modeling and speech recognition experiments on the publicly available LibriSpeech corpus.
