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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

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Showing 1–25 of 3012 papers

TitleStatusHype
Qwen2.5-Omni Technical ReportCode7
GLM-4-Voice: Towards Intelligent and Human-Like End-to-End Spoken ChatbotCode7
Scaling Speech-Text Pre-training with Synthetic Interleaved DataCode7
PaddleSpeech: An Easy-to-Use All-in-One Speech ToolkitCode6
FireRedASR: Open-Source Industrial-Grade Mandarin Speech Recognition Models from Encoder-Decoder to LLM IntegrationCode5
StreamSpeech: Simultaneous Speech-to-Speech Translation with Multi-task LearningCode5
VITA-Audio: Fast Interleaved Cross-Modal Token Generation for Efficient Large Speech-Language ModelCode4
Dolphin: A Large-Scale Automatic Speech Recognition Model for Eastern LanguagesCode4
SpeechColab Leaderboard: An Open-Source Platform for Automatic Speech Recognition EvaluationCode4
Voila: Voice-Language Foundation Models for Real-Time Autonomous Interaction and Voice Role-PlayCode3
VoiceBench: Benchmarking LLM-Based Voice AssistantsCode3
WhisperNER: Unified Open Named Entity and Speech RecognitionCode3
MooER: LLM-based Speech Recognition and Translation Models from Moore ThreadsCode3
Sentiment Reasoning for HealthcareCode3
Whisper-Flamingo: Integrating Visual Features into Whisper for Audio-Visual Speech Recognition and TranslationCode3
Self-Taught Recognizer: Toward Unsupervised Adaptation for Speech Foundation ModelsCode3
DiarizationLM: Speaker Diarization Post-Processing with Large Language ModelsCode3
Qwen-Audio: Advancing Universal Audio Understanding via Unified Large-Scale Audio-Language ModelsCode3
Delay-penalized transducer for low-latency streaming ASRCode3
Fast-MD: Fast Multi-Decoder End-to-End Speech Translation with Non-Autoregressive Hidden IntermediatesCode3
A Parallelizable Lattice Rescoring Strategy with Neural Language ModelsCode3
Conformer: Convolution-augmented Transformer for Speech RecognitionCode3
TED-LIUM 3: twice as much data and corpus repartition for experiments on speaker adaptationCode3
CleanMel: Mel-Spectrogram Enhancement for Improving Both Speech Quality and ASRCode2
LiteASR: Efficient Automatic Speech Recognition with Low-Rank ApproximationCode2
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All datasetsLRS2RealMANSagaleeHUI speech corpusLRS3-TEDM-AILabs speech datasetThe Spoken Wikipedia CorporaVoxforge GermanVoxPopuli

Benchmark Results

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