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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 251–300 of 3012 papers

TitleStatusHype
Mamba for Streaming ASR Combined with Unimodal AggregationCode1
Alignment-Free Training for Transducer-based Multi-Talker ASR—0
AfriHuBERT: A self-supervised speech representation model for African languagesCode0
Predictive Speech Recognition and End-of-Utterance Detection Towards Spoken Dialog Systems—0
Efficient Long-Form Speech Recognition for General Speech In-Context Learning—0
Fine-Tuning Automatic Speech Recognition for People with Parkinson's: An Effective Strategy for Enhancing Speech Technology Accessibility—0
A GEN AI Framework for Medical Note Generation—0
Improving Multilingual ASR in the Wild Using Simple N-best Re-rankingCode0
Are Transformers in Pre-trained LM A Good ASR Encoder? An Empirical Study—0
Deep CLAS: Deep Contextual Listen, Attend and Spell—0
MT2KD: Towards A General-Purpose Encoder for Speech, Speaker, and Audio Events—0
Speech Recognition Rescoring with Large Speech-Text Foundation Models—0
Weighted Cross-entropy for Low-Resource Languages in Multilingual Speech RecognitionCode0
Spelling Correction through Rewriting of Non-Autoregressive ASR Lattices—0
Revisiting Acoustic Features for Robust ASR—0
Bridging Speech and Text: Enhancing ASR with Pinyin-to-Character Pre-training in LLMs—0
Boosting Code-Switching ASR with Mixture of Experts Enhanced Speech-Conditioned LLM—0
Revise, Reason, and Recognize: LLM-Based Emotion Recognition via Emotion-Specific Prompts and ASR Error CorrectionCode0
MultiMed: Multilingual Medical Speech Recognition via Attention Encoder DecoderCode0
Time and Tokens: Benchmarking End-to-End Speech Dysfluency Detection—0
Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper—0
A Multimodal Dense Retrieval Approach for Speech-Based Open-Domain Question Answering—0
Personalized Speech Recognition for Children with Test-Time Adaptation—0
Channel-Aware Domain-Adaptive Generative Adversarial Network for Robust Speech RecognitionCode0
META-CAT: Speaker-Informed Speech Embeddings via Meta Information Concatenation for Multi-talker ASR—0
Large Language Models are Strong Audio-Visual Speech Recognition LearnersCode2
ASR Benchmarking: Need for a More Representative Conversational DatasetCode0
M-BEST-RQ: A Multi-Channel Speech Foundation Model for Smart Glasses—0
Chain-of-Thought Prompting for Speech Translation—0
WER We Stand: Benchmarking Urdu ASR Models—0
Ideal-LLM: Integrating Dual Encoders and Language-Adapted LLM for Multilingual Speech-to-Text—0
Zero Shot Text to Speech Augmentation for Automatic Speech Recognition on Low-Resource Accented Speech Corpora—0
SMILE: Speech Meta In-Context Learning for Low-Resource Language Automatic Speech Recognition—0
An Efficient Self-Learning Framework For Interactive Spoken Dialog Systems—0
Augmenting Automatic Speech Recognition Models with Disfluency Detection—0
Large Language Model Based Generative Error Correction: A Challenge and Baselines for Speech Recognition, Speaker Tagging, and Emotion Recognition—0
ASR Error Correction using Large Language Models—0
CPT-Boosted Wav2vec2.0: Towards Noise Robust Speech Recognition for Classroom Environments—0
Learnings from curating a trustworthy, well-annotated, and useful dataset of disordered English speech—0
Exploring SSL Discrete Tokens for Multilingual ASR—0
Exploring the Impact of Data Quantity on ASR in Extremely Low-resource Languages—0
LA-RAG:Enhancing LLM-based ASR Accuracy with Retrieval-Augmented Generation—0
Large Language Model Can Transcribe Speech in Multi-Talker Scenarios with Versatile InstructionsCode2
NEST-RQ: Next Token Prediction for Speech Self-Supervised Pre-Training—0
Full-text Error Correction for Chinese Speech Recognition with Large Language Model—0
WhisperNER: Unified Open Named Entity and Speech RecognitionCode3
Enhancing CTC-Based Visual Speech Recognition—0
Linear Time Complexity Conformers with SummaryMixing for Streaming Speech RecognitionCode0
An Effective Context-Balanced Adaptation Approach for Long-Tailed Speech Recognition—0
Keyword-Aware ASR Error Augmentation for Robust Dialogue State Tracking—0
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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