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 1001–1050 of 6433 papers

TitleStatusHype
Tiny-Align: Bridging Automatic Speech Recognition and Large Language Model on the Edge—0
Hard-Synth: Synthesizing Diverse Hard Samples for ASR using Zero-Shot TTS and LLM—0
CAFE A Novel Code switching Dataset for Algerian Dialect French and English—0
Towards Advanced Speech Signal Processing: A Statistical Perspective on Convolution-Based Architectures and its Applications—0
From Statistical Methods to Pre-Trained Models; A Survey on Automatic Speech Recognition for Resource Scarce Urdu Language—0
Whisper Finetuning on Nepali Language—0
A Novel Speech Analysis and Correction Tool for Arabic-Speaking Children—0
Inter-linguistic Phonetic Composition (IPC): A Theoretical and Computational Approach to Enhance Second Language Pronunciation—0
BanglaDialecto: An End-to-End AI-Powered Regional Speech StandardizationCode0
Systolic Arrays and Structured Pruning Co-design for Efficient Transformers in Edge Systems—0
DiMoDif: Discourse Modality-information Differentiation for Audio-visual Deepfake Detection and LocalizationCode0
Everyone deserves their voice to be heard: Analyzing Predictive Gender Bias in ASR Models Applied to Dutch Speech Data—0
Transferable Adversarial Attacks against ASR—0
DCF-DS: Deep Cascade Fusion of Diarization and Separation for Speech Recognition under Realistic Single-Channel Conditions—0
CTC-Assisted LLM-Based Contextual ASR—0
Multistage Fine-tuning Strategies for Automatic Speech Recognition in Low-resource Languages—0
SPES: Spectrogram Perturbation for Explainable Speech-to-Text Generation—0
Optimizing Contextual Speech Recognition Using Vector Quantization for Efficient Retrieval—0
Enhancing AAC Software for Dysarthric Speakers in e-Health Settings: An Evaluation Using TORGO—0
Speech is More Than Words: Do Speech-to-Text Translation Systems Leverage Prosody?—0
Augmenting Polish Automatic Speech Recognition System With Synthetic Data—0
Run-Time Adaptation of Neural Beamforming for Robust Speech Dereverberation and Denoising—0
Joint Beamforming and Speaker-Attributed ASR for Real Distant-Microphone Meeting Transcription—0
Multilingual Standalone Trustworthy Voice-Based Social Network for Disaster Situations—0
Asynchronous Tool Usage for Real-Time Agents—0
Improving Speech-based Emotion Recognition with Contextual Utterance Analysis and LLMs—0
Contextual Biasing to Improve Domain-specific Custom Vocabulary Audio Transcription without Explicit Fine-Tuning of Whisper Model—0
A Survey on Speech Large Language Models—0
Evaluating and Improving Automatic Speech Recognition Systems for Korean Meteorological Experts—0
kNN For Whisper And Its Effect On Bias And Speaker Adaptation—0
ELAICHI: Enhancing Low-resource TTS by Addressing Infrequent and Low-frequency Character Bigrams—0
DENOASR: Debiasing ASRs through Selective Denoising—0
Enhancing Low-Resource ASR through Versatile TTS: Bridging the Data Gap—0
Improving Automatic Speech Recognition with Decoder-Centric Regularisation in Encoder-Decoder Models—0
Acoustic Model Optimization over Multiple Data Sources: Merging and Valuation—0
Interventional Speech Noise Injection for ASR Generalizable Spoken Language Understanding—0
End-to-End Transformer-based Automatic Speech Recognition for Northern Kurdish: A Pioneering Approach—0
AC-Mix: Self-Supervised Adaptation for Low-Resource Automatic Speech Recognition using Agnostic Contrastive Mixup—0
Roadmap towards Superhuman Speech Understanding using Large Language Models—0
Parameter-efficient Adaptation of Multilingual Multimodal Models for Low-resource ASR—0
Failing Forward: Improving Generative Error Correction for ASR with Synthetic Data and Retrieval Augmentation—0
Computational Approaches to Arabic-English Code-Switching—0
Investigation of Speaker Representation for Target-Speaker Speech Processing—0
A Framework for Adapting Human-Robot Interaction to Diverse User GroupsCode0
Character-aware audio-visual subtitling in context—0
In-Materia Speech Recognition—0
State of NLP in Kenya: A Survey—0
Automatic Speech Recognition with BERT and CTC Transformers: A Review—0
SLAM-AAC: Enhancing Audio Captioning with Paraphrasing Augmentation and CLAP-Refine through LLMs—0
UniGlyph: A Seven-Segment Script for Universal Language Representation—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