SOTAVerified

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 28012825 of 3012 papers

TitleStatusHype
Bi-Directional Lattice Recurrent Neural Networks for Confidence EstimationCode0
When Is TTS Augmentation Through a Pivot Language Useful?Code0
Audio Segmentation for Robust Real-Time Speech Recognition Based on Neural NetworksCode0
Pretext Tasks selection for multitask self-supervised speech representation learningCode0
An Automatic Speech Recognition System for Bengali Language based on Wav2Vec2 and Transfer LearningCode0
Learning to adapt: a meta-learning approach for speaker adaptationCode0
Improving LSTM-CTC based ASR performance in domains with limited training dataCode0
Discrete Speech Unit Extraction via Independent Component AnalysisCode0
Speech Emotion Recognition with ASR Transcripts: A Comprehensive Study on Word Error Rate and Fusion TechniquesCode0
Segmentation-Free Streaming Machine TranslationCode0
Pre-training on high-resource speech recognition improves low-resource speech-to-text translationCode0
Discrete Cross-Modal Alignment Enables Zero-Shot Speech TranslationCode0
Speech-enhanced and Noise-aware Networks for Robust Speech RecognitionCode0
Discovering Phonetic Inventories with Crosslingual Automatic Speech RecognitionCode0
Selective Attention Merging for low resource tasks: A case study of Child ASRCode0
Improving CTC-based speech recognition via knowledge transferring from pre-trained language modelsCode0
A Comparative Study on Transformer vs RNN in Speech ApplicationsCode0
ASDF: A Differential Testing Framework for Automatic Speech Recognition SystemsCode0
Swiss Parliaments Corpus, an Automatically Aligned Swiss German Speech to Standard German Text CorpusCode0
Adapting the adapters for code-switching in multilingual ASRCode0
Improving Automatic Speech Recognition for Non-Native English with Transfer Learning and Language Model DecodingCode0
Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech RecognitionCode0
Let SSMs be ConvNets: State-space Modeling with Optimal Tensor ContractionsCode0
Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin ChineseCode0
Leveraging Broadcast Media Subtitle Transcripts for Automatic Speech Recognition and SubtitlingCode0
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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