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

Showing 12011250 of 6433 papers

TitleStatusHype
COSMIC: Data Efficient Instruction-tuning For Speech In-Context Learning0
Server-side Rescoring of Spoken Entity-centric Knowledge Queries for Virtual Assistants0
Multilingual DistilWhisper: Efficient Distillation of Multi-task Speech Models via Language-Specific ExpertsCode1
Automatic Disfluency Detection from Untranscribed SpeechCode1
Distil-Whisper: Robust Knowledge Distillation via Large-Scale Pseudo LabellingCode4
End-to-End Single-Channel Speaker-Turn Aware Conversational Speech TranslationCode1
RIR-SF: Room Impulse Response Based Spatial Feature for Target Speech Recognition in Multi-Channel Multi-Speaker Scenarios0
Combining Language Models For Specialized Domains: A Colorful Approach0
MUST: A Multilingual Student-Teacher Learning approach for low-resource speech recognition0
MixRep: Hidden Representation Mixup for Low-Resource Speech RecognitionCode0
TorchAudio 2.1: Advancing speech recognition, self-supervised learning, and audio processing components for PyTorchCode4
Developing a Multilingual Dataset and Evaluation Metrics for Code-Switching: A Focus on Hong Kong's Polylingual DynamicsCode1
Unified Segment-to-Segment Framework for Simultaneous Sequence Generation0
Dialect Adaptation and Data Augmentation for Low-Resource ASR: TalTech Systems for the MADASR 2023 Challenge0
UniX-Encoder: A Universal X-Channel Speech Encoder for Ad-Hoc Microphone Array Speech Processing0
DISCO: A Large Scale Human Annotated Corpus for Disfluency Correction in Indo-European LanguagesCode0
CL-MASR: A Continual Learning Benchmark for Multilingual ASRCode1
Back Transcription as a Method for Evaluating Robustness of Natural Language Understanding Models to Speech Recognition ErrorsCode0
ArTST: Arabic Text and Speech TransformerCode1
How Much Context Does My Attention-Based ASR System Need?Code1
Accented Speech Recognition With Accent-specific CodebooksCode1
Modality Dropout for Multimodal Device Directed Speech Detection using Verbal and Non-Verbal Features0
Key Frame Mechanism For Efficient Conformer Based End-to-end Speech RecognitionCode0
Quantifying the Dialect Gap and its Correlates Across Languages0
Leveraging Timestamp Information for Serialized Joint Streaming Recognition and Translation0
Delayed Memory Unit: Modelling Temporal Dependency Through Delay GateCode0
Conversational Speech Recognition by Learning Audio-textual Cross-modal Contextual Representation0
Intelligibility prediction with a pretrained noise-robust automatic speech recognition model0
SALMONN: Towards Generic Hearing Abilities for Large Language ModelsCode3
The CHiME-7 Challenge: System Description and Performance of NeMo Team's DASR System0
Unintended Memorization in Large ASR Models, and How to Mitigate It0
Audio-AdapterFusion: A Task-ID-free Approach for Efficient and Non-Destructive Multi-task Speech Recognition0
Generative error correction for code-switching speech recognition using large language models0
Iterative Shallow Fusion of Backward Language Model for End-to-End Speech Recognition0
Multi-stage Large Language Model Correction for Speech Recognition0
Advanced accent/dialect identification and accentedness assessment with multi-embedding models and automatic speech recognition0
Correction Focused Language Model Training for Speech Recognition0
Zipformer: A faster and better encoder for automatic speech recognition0
Long-form Simultaneous Speech Translation: Thesis Proposal0
VoxArabica: A Robust Dialect-Aware Arabic Speech Recognition System0
Detecting Speech Abnormalities with a Perceiver-based Sequence Classifier that Leverages a Universal Speech Model0
Personalization of CTC-based End-to-End Speech Recognition Using Pronunciation-Driven Subword Tokenization0
Optimized Tokenization for Transcribed Error Correction0
End-to-end Multichannel Speaker-Attributed ASR: Speaker Guided Decoder and Input Feature Analysis0
Large Vocabulary Spontaneous Speech Recognition for Tigrigna0
Homophone Disambiguation Reveals Patterns of Context Mixing in Speech TransformersCode0
Advancing Test-Time Adaptation in Wild Acoustic Test SettingsCode1
Improved Contextual Recognition In Automatic Speech Recognition Systems By Semantic Lattice Rescoring0
SALM: Speech-augmented Language Model with In-context Learning for Speech Recognition and Translation0
Fast Word Error Rate Estimation Using Self-Supervised Representations for Speech and Text0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1AmNetWord Error Rate (WER)8.6Unverified
2HMM-(SAT)GMMWord Error Rate (WER)8Unverified
3Local Prior Matching (Large Model)Word Error Rate (WER)7.19Unverified
4SnipsWord Error Rate (WER)6.4Unverified
5Li-GRUWord Error Rate (WER)6.2Unverified
6HMM-DNN + pNorm*Word Error Rate (WER)5.5Unverified
7CTC + policy learningWord Error Rate (WER)5.42Unverified
8Deep Speech 2Word Error Rate (WER)5.33Unverified
9Gated ConvNetsWord Error Rate (WER)4.8Unverified
10HMM-TDNN + iVectorsWord Error Rate (WER)4.8Unverified
#ModelMetricClaimedVerifiedStatus
1Local Prior Matching (Large Model)Word Error Rate (WER)20.84Unverified
2SnipsWord Error Rate (WER)16.5Unverified
3Local Prior Matching (Large Model, ConvLM LM)Word Error Rate (WER)15.28Unverified
4Deep Speech 2Word Error Rate (WER)13.25Unverified
5TDNN + pNorm + speed up/down speechWord Error Rate (WER)12.5Unverified
6CTC-CRF 4gram-LMWord Error Rate (WER)10.65Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)10.47Unverified
8MT4SSLWord Error Rate (WER)9.6Unverified
9Jasper DR 10x5Word Error Rate (WER)8.79Unverified
10EspressoWord Error Rate (WER)8.7Unverified
#ModelMetricClaimedVerifiedStatus
1Deep SpeechPercentage error20Unverified
2DNN-HMMPercentage error18.5Unverified
3CD-DNNPercentage error16.1Unverified
4DNNPercentage error16Unverified
5DNN + DropoutPercentage error15Unverified
6DNN BMMIPercentage error12.9Unverified
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
8DNN MPEPercentage error12.9Unverified
9DNN MMIPercentage error12.9Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage error12.6Unverified
#ModelMetricClaimedVerifiedStatus
1LSNNPercentage error33.2Unverified
2LAS multitask with indicators samplingPercentage error20.4Unverified
3Soft Monotonic Attention (ours, offline)Percentage error20.1Unverified
4QCNN-10L-256FMPercentage error19.64Unverified
5Bi-LSTM + skip connections w/ CTCPercentage error17.7Unverified
6Bi-RNN + AttentionPercentage error17.6Unverified
7RNN-CRF on 24(x3) MFSCPercentage error17.3Unverified
8CNN in time and frequency + dropout, 17.6% w/o dropoutPercentage error16.7Unverified
9Light Gated Recurrent UnitsPercentage error16.7Unverified
10GRUPercentage error16.6Unverified
#ModelMetricClaimedVerifiedStatus
1AttWord Error Rate (WER)18.7Unverified
2CTC/AttWord Error Rate (WER)6.7Unverified
3BRA-EWord Error Rate (WER)6.63Unverified
4CTC-CRF 4gram-LMWord Error Rate (WER)6.34Unverified
5BATWord Error Rate (WER)4.97Unverified
6ParaformerWord Error Rate (WER)4.95Unverified
7U2Word Error Rate (WER)4.72Unverified
8UMAWord Error Rate (WER)4.7Unverified
9Lightweight TransducerWord Error Rate (WER)4.31Unverified
10CIF-HKD With LMWord Error Rate (WER)4.1Unverified
#ModelMetricClaimedVerifiedStatus
1Jasper 10x3Word Error Rate (WER)6.9Unverified
2CNN over RAW speech (wav)Word Error Rate (WER)5.6Unverified
3CTC-CRF 4gram-LMWord Error Rate (WER)3.79Unverified
4Deep Speech 2Word Error Rate (WER)3.6Unverified
5test-set on open vocabulary (i.e. harder), model = HMM-DNN + pNorm*Word Error Rate (WER)3.6Unverified
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5Unverified
7Convolutional Speech RecognitionWord Error Rate (WER)3.5Unverified
8EspressoWord Error Rate (WER)3.4Unverified
9CTC-CRF VGG-BLSTMWord Error Rate (WER)3.2Unverified
10Transformer with Relaxed AttentionWord Error Rate (WER)3.19Unverified