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Found 13 papers, 2 papers with code
Hybrid Attention-based Encoder-decoder Model for Efficient Language Model Adaptation
Attention-based encoder-decoder (AED) speech recognition model has been widely successful in recent years.
Adapting Large Language Model with Speech for Fully Formatted End-to-End Speech Recognition
Most end-to-end (E2E) speech recognition models are composed of encoder and decoder blocks that perform acoustic and language modeling functions.
Acoustic-aware Non-autoregressive Spell Correction with Mask Sample Decoding
As a result, we reduce the WER of a streaming TT from 7. 6% to 6. 5% on the Librispeech test-other data and the CER from 7. 3% to 6. 1% on the Aishell test data, respectively.
Have best of both worlds: two-pass hybrid and E2E cascading framework for speech recognition
Hybrid and end-to-end (E2E) systems have their individual advantages, with different error patterns in the speech recognition results.
Minimum Word Error Rate Training with Language Model Fusion for End-to-End Speech Recognition
In this work, we perform LM fusion in the minimum WER (MWER) training of an E2E model to obviate the need for LM weights tuning during inference.
End-to-End Speaker-Attributed ASR with Transformer
This paper presents our recent effort on end-to-end speaker-attributed automatic speech recognition, which jointly performs speaker counting, speech recognition and speaker identification for monaural multi-talker audio.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Large-Scale Pre-Training of End-to-End Multi-Talker ASR for Meeting Transcription with Single Distant Microphone
Transcribing meetings containing overlapped speech with only a single distant microphone (SDM) has been one of the most challenging problems for automatic speech recognition (ASR).
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Low Latency End-to-End Streaming Speech Recognition with a Scout Network
The attention-based Transformer model has achieved promising results for speech recognition (SR) in the offline mode.
Audio and Speech Processing
Semantic Mask for Transformer based End-to-End Speech Recognition
Attention-based encoder-decoder model has achieved impressive results for both automatic speech recognition (ASR) and text-to-speech (TTS) tasks.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+2
Advancing Acoustic-to-Word CTC Model with Attention and Mixed-Units
In particular, we introduce Attention CTC, Self-Attention CTC, Hybrid CTC, and Mixed-unit CTC.
Developing Far-Field Speaker System Via Teacher-Student Learning
In this study, we develop the keyword spotting (KWS) and acoustic model (AM) components in a far-field speaker system.
Advancing Acoustic-to-Word CTC Model
However, the word-based CTC model suffers from the out-of-vocabulary (OOV) issue as it can only model limited number of words in the output layer and maps all the remaining words into an OOV output node.
Acoustic-To-Word Model Without OOV
However, this type of word-based CTC model suffers from the out-of-vocabulary (OOV) issue as it can only model limited number of words in the output layer and maps all the remaining words into an OOV output node.
