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Automatic Speech Recognition (ASR)

Automatic Speech Recognition (ASR) involves converting spoken language into written text. It is designed to transcribe spoken words into text in real-time, allowing people to communicate with computers, mobile devices, and other technology using their voice. The goal of Automatic Speech Recognition is to accurately transcribe speech, taking into account variations in accent, pronunciation, and speaking style, as well as background noise and other factors that can affect speech quality.

Papers

Showing 13261350 of 3012 papers

TitleStatusHype
Hear No Evil: Towards Adversarial Robustness of Automatic Speech Recognition via Multi-Task Learning0
A Complementary Joint Training Approach Using Unpaired Speech and Text for Low-Resource Automatic Speech Recognition0
Deliberation Model for On-Device Spoken Language Understanding0
An Analysis of Semantically-Aligned Speech-Text Embeddings0
Cross-lingual Self-Supervised Speech Representations for Improved Dysarthric Speech Recognition0
A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems0
Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation0
End-to-end model for named entity recognition from speech without paired training data0
PriMock57: A Dataset Of Primary Care Mock ConsultationsCode1
Alternate Intermediate Conditioning with Syllable-level and Character-level Targets for Japanese ASR0
Text-To-Speech Data Augmentation for Low Resource Speech Recognition0
Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding0
End-to-End Multi-speaker ASR with Independent Vector Analysis0
End-to-End Integration of Speech Recognition, Speech Enhancement, and Self-Supervised Learning Representation0
Probing Speech Emotion Recognition Transformers for Linguistic Knowledge0
Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition0
Importance of Different Temporal Modulations of Speech: A Tale of Two Perspectives0
Effectiveness of text to speech pseudo labels for forced alignment and cross lingual pretrained models for low resource speech recognition0
A Comparative Study on Speaker-attributed Automatic Speech Recognition in Multi-party Meetings0
Analyzing the factors affecting usefulness of Self-Supervised Pre-trained Representations for Speech Recognition0
Memory-Efficient Training of RNN-Transducer with Sampled Softmax0
A Hybrid Continuity Loss to Reduce Over-Suppression for Time-domain Target Speaker ExtractionCode1
indic-punct: An automatic punctuation restoration and inverse text normalization framework for Indic languagesCode1
Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data0
How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control CommunicationsCode1
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1TM-CTCTest WER10.1Unverified
2TM-seq2seqTest WER9.7Unverified
3CTC/attentionTest WER8.2Unverified
4LF-MMI TDNNTest WER6.7Unverified
5Whisper-LLaMATest WER6.6Unverified
6End2end ConformerTest WER3.9Unverified
7End2end ConformerTest WER3.7Unverified
8MoCo + wav2vec (w/o extLM)Test WER2.7Unverified
9CTC/AttentionTest WER1.5Unverified
10WhisperTest WER1.3Unverified
#ModelMetricClaimedVerifiedStatus
1SpatialNetCER14.5Unverified
2CleanMel-L-maskCER14.4Unverified
#ModelMetricClaimedVerifiedStatus
1ConformerTest WER15.32Unverified
2Whisper-largev3-finetunedTest WER10.82Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)1.89Unverified
#ModelMetricClaimedVerifiedStatus
1DistillAVWER1.4Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)4.28Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)8.04Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer TransducerWER (%)3.36Unverified
#ModelMetricClaimedVerifiedStatus
1Conformer Transducer (German)WER (%)8.98Unverified