SOTAVerified

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 3951–4000 of 6433 papers

TitleStatusHype
SALAD: Smart AI Language Assistant Daily—0
SALMONN-omni: A Codec-free LLM for Full-duplex Speech Understanding and Generation—0
SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation—0
Samba-ASR: State-Of-The-Art Speech Recognition Leveraging Structured State-Space Models—0
Sample-Efficient Unsupervised Domain Adaptation of Speech Recognition Systems A case study for Modern Greek—0
Samrómur Children: An Icelandic Speech Corpus—0
Samr\'omur: Crowd-sourcing Data Collection for Icelandic Speech Recognition—0
Samrómur: Crowd-sourcing large amounts of data—0
SAN: a robust end-to-end ASR model architecture—0
SANTLR: Speech Annotation Toolkit for Low Resource Languages—0
SapAugment: Learning A Sample Adaptive Policy for Data Augmentation—0
SAVAS: Collecting, Annotating and Sharing Audiovisual Language Resources for Automatic Subtitling—0
Saving RNN Computations with a Neuron-Level Fuzzy Memoization Scheme—0
SAWDUST: a Semi-Automated Wizard Dialogue Utterance Selection Tool for domain-independent large-domain dialogue—0
CIF-T: A Novel CIF-based Transducer Architecture for Automatic Speech Recognition—0
Scalable and Accurate Self-supervised Multimodal Representation Learning without Aligned Video and Text Data—0
Scalable End-to-End RF Classification: A Case Study on Undersized Dataset Regularization by Convolutional-MST—0
Scalable language model adaptation for spoken dialogue systems—0
Scalable Multi Corpora Neural Language Models for ASR—0
SCaLa: Supervised Contrastive Learning for End-to-End Speech Recognition—0
Scaling and Enhancing LLM-based AVSR: A Sparse Mixture of Projectors Approach—0
Scaling A Simple Approach to Zero-Shot Speech Recognition—0
Scaling ASR Improves Zero and Few Shot Learning—0
Scaling Auditory Cognition via Test-Time Compute in Audio Language Models—0
Scaling Laws for Discriminative Speech Recognition Rescoring Models—0
Scaling Semantic Frame Annotation—0
Scaling sparsemax based channel selection for speech recognition with ad-hoc microphone arrays—0
Scaling Speech Enhancement in Unseen Environments with Noise Embeddings—0
Scaling Up Deliberation for Multilingual ASR—0
Scaling Up Online Speech Recognition Using ConvNets—0
Scan, Attend and Read: End-to-End Handwritten Paragraph Recognition with MDLSTM Attention—0
SCDiar: a streaming diarization system based on speaker change detection and speech recognition—0
Scenario Aware Speech Recognition: Advancements for Apollo Fearless Steps & CHiME-4 Corpora—0
Scene-aware Far-field Automatic Speech Recognition—0
Scene Text Recognition from Two-Dimensional Perspective—0
SC-MoE: Switch Conformer Mixture of Experts for Unified Streaming and Non-streaming Code-Switching ASR—0
Scoring Spoken Responses Based on Content Accuracy—0
SC-SOT: Conditioning the Decoder on Diarized Speaker Information for End-to-End Overlapped Speech Recognition—0
SEAL: Speech Embedding Alignment Learning for Speech Large Language Model with Retrieval-Augmented Generation—0
Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks—0
Search Intelligence: Deep Learning For Dominant Category Prediction—0
SE-Bridge: Speech Enhancement with Consistent Brownian Bridge—0
Security and Privacy Problems in Voice Assistant Applications: A Survey—0
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition—0
Seed Words Based Data Selection for Language Model Adaptation—0
Seewo's Submission to MLC-SLM: Lessons learned from Speech Reasoning Language Models—0
Segmental Recurrent Neural Networks for End-to-end Speech Recognition—0
Segmentation Strategies for Streaming Speech Translation—0
Segment-Level Vectorized Beam Search Based on Partially Autoregressive Inference—0
Self-Attention Aligner: A Latency-Control End-to-End Model for ASR Using Self-Attention Network and Chunk-Hopping—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