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 1351–1400 of 6433 papers

TitleStatusHype
PolySpeech: Exploring Unified Multitask Speech Models for Competitiveness with Single-task Models—0
ML-SUPERB 2.0: Benchmarking Multilingual Speech Models Across Modeling Constraints, Languages, and Datasets—0
Comparative Analysis of Personalized Voice Activity Detection Systems: Assessing Real-World Effectiveness—0
PRoDeliberation: Parallel Robust Deliberation for End-to-End Spoken Language Understanding—0
Refining Self-Supervised Learnt Speech Representation using Brain Activations—0
Transformer-based Model for ASR N-Best Rescoring and Rewriting—0
Spoken Language Corpora Augmentation with Domain-Specific Voice-Cloned Speech—0
Reading Miscue Detection in Primary School through Automatic Speech Recognition—0
Fast Context-Biasing for CTC and Transducer ASR models with CTC-based Word Spotter—0
AS-70: A Mandarin stuttered speech dataset for automatic speech recognition and stuttering event detection—0
Tag and correct: high precision post-editing approach to correction of speech recognition errors—0
Label-Looping: Highly Efficient Decoding for Transducers—0
Synthetic Query Generation using Large Language Models for Virtual Assistants—0
ASTRA: Aligning Speech and Text Representations for Asr without Sampling—0
A Parameter-efficient Language Extension Framework for Multilingual ASR—0
MS-HuBERT: Mitigating Pre-training and Inference Mismatch in Masked Language Modelling methods for learning Speech Representations—0
Do Prompts Really Prompt? Exploring the Prompt Understanding Capability of WhisperCode0
Optimizing Multi-Stuttered Speech Classification: Leveraging Whisper's Encoder for Efficient Parameter Reduction in Automated Assessment—0
Pitch-Aware RNN-T for Mandarin Chinese Mispronunciation Detection and Diagnosis—0
LoRA-Whisper: Parameter-Efficient and Extensible Multilingual ASR—0
Hypernetworks for Personalizing ASR to Atypical Speech—0
Helsinki Speech Challenge 2024—0
To Distill or Not to Distill? On the Robustness of Robust Knowledge DistillationCode0
Speed of Light Exact Greedy Decoding for RNN-T Speech Recognition Models on GPU—0
Improving Zero-Shot Chinese-English Code-Switching ASR with kNN-CTC and Gated Monolingual Datastores—0
Flexible Multichannel Speech Enhancement for Noise-Robust Frontend—0
Text Injection for Neural Contextual Biasing—0
Error-preserving Automatic Speech Recognition of Young English Learners' LanguageCode0
Joint Beam Search Integrating CTC, Attention, and Transducer Decoders—0
Enhancing CTC-based speech recognition with diverse modeling units—0
Task Arithmetic can Mitigate Synthetic-to-Real Gap in Automatic Speech Recognition—0
Whistle: Data-Efficient Multilingual and Crosslingual Speech Recognition via Weakly Phonetic Supervision—0
Efficiently Train ASR Models that Memorize Less and Perform Better with Per-core Clipping—0
Discrete Multimodal Transformers with a Pretrained Large Language Model for Mixed-Supervision Speech Processing—0
Keyword-Guided Adaptation of Automatic Speech Recognition—0
Enabling ASR for Low-Resource Languages: A Comprehensive Dataset Creation Approach—0
Compute-Efficient Medical Image Classification with Softmax-Free Transformers and Sequence Normalization—0
YODAS: Youtube-Oriented Dataset for Audio and Speech—0
Wav2Prompt: End-to-End Speech Prompt Generation and Tuning For LLM in Zero and Few-shot Learning—0
Zipper: A Multi-Tower Decoder Architecture for Fusing Modalities—0
Augmented Conversation with Embedded Speech-Driven On-the-Fly Referencing in AR—0
Intelligent Clinical Documentation: Harnessing Generative AI for Patient-Centric Clinical Note Generation—0
NUTS, NARS, and Speech—0
Federating Dynamic Models using Early-Exit Architectures for Automatic Speech Recognition on Heterogeneous ClientsCode0
Denoising LM: Pushing the Limits of Error Correction Models for Speech Recognition—0
Contrastive and Consistency Learning for Neural Noisy-Channel Model in Spoken Language UnderstandingCode0
You don't understand me!: Comparing ASR results for L1 and L2 speakers of Swedish—0
ST-Gait++: Leveraging spatio-temporal convolutions for gait-based emotion recognition on videos—0
Joint Optimization of Streaming and Non-Streaming Automatic Speech Recognition with Multi-Decoder and Knowledge Distillation—0
Contextualized Automatic Speech Recognition with Dynamic Vocabulary—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