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Speech Recognition

Speech Recognition is the task of converting spoken language into text. It involves recognizing the words spoken in an audio recording and transcribing them into a written format. The goal is to accurately transcribe the speech in real-time or from recorded audio, taking into account factors such as accents, speaking speed, and background noise.

( Image credit: SpecAugment )

Papers

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Showing 501–550 of 6433 papers

TitleStatusHype
MOSEL: 950,000 Hours of Speech Data for Open-Source Speech Foundation Model Training on EU LanguagesCode2
Mamba for Streaming ASR Combined with Unimodal AggregationCode1
Predictive Speech Recognition and End-of-Utterance Detection Towards Spoken Dialog Systems—0
Alignment-Free Training for Transducer-based Multi-Talker ASR—0
AfriHuBERT: A self-supervised speech representation model for African languagesCode0
Boosting Hybrid Autoregressive Transducer-based ASR with Internal Acoustic Model Training and Dual Blank Thresholding—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
Quantitative Analysis of Audio-Visual Tasks: An Information-Theoretic Perspective—0
CoT-ST: Enhancing LLM-based Speech Translation with Multimodal Chain-of-Thought—0
Advanced Clustering Techniques for Speech Signal Enhancement: A Review and Metanalysis of Fuzzy C-Means, K-Means, and Kernel Fuzzy C-Means Methods—0
A GEN AI Framework for Medical Note Generation—0
Speech-Mamba: Long-Context Speech Recognition with Selective State Spaces Models—0
Improving Multilingual ASR in the Wild Using Simple N-best Re-ranking—0
Paraformer-v2: An improved non-autoregressive transformer for noise-robust speech recognition—0
Unveiling the Role of Pretraining in Direct Speech Translation—0
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
Weighted Cross-entropy for Low-Resource Languages in Multilingual Speech RecognitionCode0
Speech Recognition Rescoring with Large Speech-Text Foundation Models—0
Semi-Supervised Cognitive State Classification from Speech with Multi-View Pseudo-LabelingCode0
How to Connect Speech Foundation Models and Large Language Models? What Matters and What Does Not—0
WeSep: A Scalable and Flexible Toolkit Towards Generalizable Target Speaker ExtractionCode3
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
Hypothesis Clustering and Merging: Novel MultiTalker Speech Recognition with Speaker Tokens—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
Strong Alone, Stronger Together: Synergizing Modality-Binding Foundation Models with Optimal Transport for Non-Verbal Emotion Recognition—0
MultiMed: Multilingual Medical Speech Recognition via Attention Encoder Decoder—0
Time and Tokens: Benchmarking End-to-End Speech Dysfluency Detection—0
Large Language Model Should Understand Pinyin for Chinese ASR Error Correction—0
Fast Streaming Transducer ASR Prototyping via Knowledge Distillation with Whisper—0
LM-assisted keyword biasing with Aho-Corasick algorithm for Transducer-based ASR—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
Robust Audiovisual Speech Recognition Models with Mixture-of-Experts—0
Disentangling Speakers in Multi-Talker Speech Recognition with Speaker-Aware CTCCode1
Enhancing Synthetic Training Data for Speech Commands: From ASR-Based Filtering to Domain Adaptation in SSL Latent Space—0
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
A Joint Spectro-Temporal Relational Thinking Based Acoustic Modeling Framework—0
Moshi: a speech-text foundation model for real-time dialogueCode9
Ideal-LLM: Integrating Dual Encoders and Language-Adapted LLM for Multilingual Speech-to-Text—0
Chain-of-Thought Prompting for Speech Translation—0
WER We Stand: Benchmarking Urdu ASR Models—0
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All datasetsLibriSpeech test-cleanLibriSpeech test-otherSwitchboard + Hub500TIMITAISHELL-1WSJ eval92Common Voice Germanswb_hub_500 WER fullSWBCHTUDACommon Voice FrenchCommon Voice SpanishMediaSpeech

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6—Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8—Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19—Unverified
4SnipsWord Error Rate (WER)6.4—Unverified
5Li-GRUWord Error Rate (WER)6.2—Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5—Unverified
7CTC + policy learningWord Error Rate (WER)5.42—Unverified
8Deep Speech 2Word Error Rate (WER)5.33—Unverified
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8—Unverified
10Gated ConvNetsWord Error Rate (WER)4.8—Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84—Unverified
2SnipsWord Error Rate (WER)16.5—Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28—Unverified
4Deep Speech 2Word Error Rate (WER)13.25—Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5—Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65—Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47—Unverified
8MT4SSLWord Error Rate (WER)9.6—Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79—Unverified
10EspressoWord Error Rate (WER)8.7—Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20—Unverified
2DNN-HMMPercentage error18.5—Unverified
3CD-DNNPercentage error16.1—Unverified
4DNNPercentage error16—Unverified
5DNN + DropoutPercentage error15—Unverified
6DNN BMMIPercentage error12.9—Unverified
7DNN MPEPercentage error12.9—Unverified
8DNN MMIPercentage error12.9—Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
10HMM-DNN +sMBRPercentage error12.6—Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2—Unverified
2LAS multitask with indicators samplingPercentage error20.4—Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1—Unverified
4QCNN-10L-256FMPercentage error19.64—Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7—Unverified
6Bi-RNN + AttentionPercentage error17.6—Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3—Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7—Unverified
9Light Gated Recurrent UnitsPercentage error16.7—Unverified
10GRUPercentage error16.6—Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7—Unverified
2CTC/AttWord Error Rate (WER)6.7—Unverified
3BRA-EWord Error Rate (WER)6.63—Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34—Unverified
5BATWord Error Rate (WER)4.97—Unverified
6ParaformerWord Error Rate (WER)4.95—Unverified
7U2Word Error Rate (WER)4.72—Unverified
8UMAWord Error Rate (WER)4.7—Unverified
9Lightweight TransducerWord Error Rate (WER)4.31—Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1—Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9—Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6—Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79—Unverified
4Deep Speech 2Word Error Rate (WER)3.6—Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6—Unverified
6Convolutional Speech RecognitionWord Error Rate (WER)3.5—Unverified
7TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
8EspressoWord Error Rate (WER)3.4—Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2—Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19—Unverified