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 701–750 of 6433 papers

TitleStatusHype
Empowering Whisper as a Joint Multi-Talker and Target-Talker Speech Recognition SystemCode1
Tamil Language Computing: the Present and the Future—0
Dynamic Encoder Size Based on Data-Driven Layer-wise Pruning for Speech Recognition—0
Explaining Spectrograms in Machine Learning: A Study on Neural Networks for Speech ClassificationCode0
HebDB: a Weakly Supervised Dataset for Hebrew Speech Processing—0
Evaluating Voice Command Pipelines for Drone Control: From STT and LLM to Direct Classification and Siamese Networks—0
A voice and speech corpus of patients who underwent upper airway surgery in pre- and post-operative statesCode0
Tailored Design of Audio-Visual Speech Recognition Models using BranchformersCode1
Analyzing Speech Unit Selection for Textless Speech-to-Speech Translation—0
Homogeneous Speaker Features for On-the-Fly Dysarthric and Elderly Speaker Adaptation—0
Morse Code-Enabled Speech Recognition for Individuals with Visual and Hearing Impairments—0
CosyVoice: A Scalable Multilingual Zero-shot Text-to-speech Synthesizer based on Supervised Semantic TokensCode11
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition—0
XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models—0
Semi-supervised Learning for Code-Switching ASR with Large Language Model Filter—0
LearnerVoice: A Dataset of Non-Native English Learners' Spontaneous Speech—0
Written Term Detection Improves Spoken Term DetectionCode0
Pretraining End-to-End Keyword Search with Automatically Discovered Acoustic UnitsCode2
Romanization Encoding For Multilingual ASR—0
Performance Analysis of Speech Encoders for Low-Resource SLU and ASR in Tunisian Dialect—0
Multitaper mel-spectrograms for keyword spotting—0
Controlling Whisper: Universal Acoustic Adversarial Attacks to Control Speech Foundation ModelsCode1
Speculative Speech Recognition by Audio-Prefixed Low-Rank Adaptation of Language Models—0
Finetuning End-to-End Models for Estonian Conversational Spoken Language Translation—0
Learning Video Temporal Dynamics with Cross-Modal Attention for Robust Audio-Visual Speech RecognitionCode1
Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis—0
FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMsCode11
Improving Self-supervised Pre-training using Accent-Specific CodebooksCode1
Serialized Output Training by Learned Dominance—0
Multi-Convformer: Extending Conformer with Multiple Convolution Kernels—0
Self-supervised ASR Models and Features For Dysarthric and Elderly Speech Recognition—0
Codec-ASR: Training Performant Automatic Speech Recognition Systems with Discrete Speech Representations—0
Advanced Framework for Animal Sound Classification With Features Optimization—0
Qifusion-Net: Layer-adapted Stream/Non-stream Model for End-to-End Multi-Accent Speech Recognition—0
The USTC-NERCSLIP Systems for The ICMC-ASR Challenge—0
Towards the Next Frontier in Speech Representation Learning Using Disentanglement—0
Pinyin Regularization in Error Correction for Chinese Speech Recognition with Large Language ModelsCode1
Toward Automated Detection of Biased Social Signals from the Content of Clinical Conversations—0
Cross-Lingual Transfer Learning for Speech Translation—0
Less Forgetting for Better Generalization: Exploring Continual-learning Fine-tuning Methods for Speech Self-supervised Representations—0
Error Correction by Paying Attention to Both Acoustic and Confidence References for Automatic Speech Recognition—0
Open-Source Conversational AI with SpeechBrain 1.0—0
Less is More: Accurate Speech Recognition & Translation without Web-Scale Data—0
Voices Unheard: NLP Resources and Models for Yorùbá Regional DialectsCode0
Tradition or Innovation: A Comparison of Modern ASR Methods for Forced Alignment—0
Enhanced ASR Robustness to Packet Loss with a Front-End Adaptation NetworkCode0
Applying LLMs for Rescoring N-best ASR Hypotheses of Casual Conversations: Effects of Domain Adaptation and Context Carry-over—0
SC-MoE: Switch Conformer Mixture of Experts for Unified Streaming and Non-streaming Code-Switching ASR—0
Dynamic Data Pruning for Automatic Speech Recognition—0
Automatic Speech Recognition for Hindi—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
9Gated ConvNetsWord Error Rate (WER)4.8—Unverified
10HMM-TDNN + iVectorsWord 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
7HMM-TDNN + pNorm + speed up/down speechPercentage error12.9—Unverified
8DNN MPEPercentage error12.9—Unverified
9DNN MMIPercentage error12.9—Unverified
10CNN + Bi-RNN + CTC (speech to letters), 25.9% WER if trainedonlyon SWBPercentage 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
6TC-DNN-BLSTM-DNNWord Error Rate (WER)3.5—Unverified
7Convolutional Speech RecognitionWord 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