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 1251–1300 of 6433 papers

TitleStatusHype
Text-Based Detection of On-Hold Scripts in Contact Center CallsCode0
Tamil Language Computing: the Present and the Future—0
Explaining Spectrograms in Machine Learning: A Study on Neural Networks for Speech ClassificationCode0
Dynamic Encoder Size Based on Data-Driven Layer-wise Pruning for Speech Recognition—0
Evaluating Voice Command Pipelines for Drone Control: From STT and LLM to Direct Classification and Siamese Networks—0
HebDB: a Weakly Supervised Dataset for Hebrew Speech Processing—0
A voice and speech corpus of patients who underwent upper airway surgery in pre- and post-operative statesCode0
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
Multitaper mel-spectrograms for keyword spotting—0
LearnerVoice: A Dataset of Non-Native English Learners' Spontaneous Speech—0
Speculative Speech Recognition by Audio-Prefixed Low-Rank Adaptation of Language Models—0
Romanization Encoding For Multilingual ASR—0
Written Term Detection Improves Spoken Term DetectionCode0
Performance Analysis of Speech Encoders for Low-Resource SLU and ASR in Tunisian Dialect—0
Semi-supervised Learning for Code-Switching ASR with Large Language Model Filter—0
XLSR-Transducer: Streaming ASR for Self-Supervised Pretrained Models—0
Seed-ASR: Understanding Diverse Speech and Contexts with LLM-based Speech Recognition—0
Improving Accented Speech Recognition using Data Augmentation based on Unsupervised Text-to-Speech Synthesis—0
Serialized Output Training by Learned Dominance—0
Multi-Convformer: Extending Conformer with Multiple Convolution Kernels—0
Finetuning End-to-End Models for Estonian Conversational Spoken Language Translation—0
Qifusion-Net: Layer-adapted Stream/Non-stream Model for End-to-End Multi-Accent Speech Recognition—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
The USTC-NERCSLIP Systems for The ICMC-ASR Challenge—0
Towards the Next Frontier in Speech Representation Learning Using Disentanglement—0
Cross-Lingual Transfer Learning for Speech Translation—0
Toward Automated Detection of Biased Social Signals from the Content of Clinical Conversations—0
Less Forgetting for Better Generalization: Exploring Continual-learning Fine-tuning Methods for Speech Self-supervised Representations—0
Open-Source Conversational AI with SpeechBrain 1.0—0
Error Correction by Paying Attention to Both Acoustic and Confidence References for Automatic Speech Recognition—0
Less is More: Accurate Speech Recognition & Translation without Web-Scale Data—0
Enhanced ASR Robustness to Packet Loss with a Front-End Adaptation NetworkCode0
Tradition or Innovation: A Comparison of Modern ASR Methods for Forced Alignment—0
Applying LLMs for Rescoring N-best ASR Hypotheses of Casual Conversations: Effects of Domain Adaptation and Context Carry-over—0
Voices Unheard: NLP Resources and Models for Yorùbá Regional DialectsCode0
Automatic Speech Recognition for Hindi—0
MSR-86K: An Evolving, Multilingual Corpus with 86,300 Hours of Transcribed Audio for Speech Recognition Research—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
Sequential Editing for Lifelong Training of Speech Recognition Models—0
MSRS: Training Multimodal Speech Recognition Models from Scratch with Sparse Mask Optimization—0
FASA: a Flexible and Automatic Speech Aligner for Extracting High-quality Aligned Children Speech DataCode0
A Comprehensive Solution to Connect Speech Encoder and Large Language Model for ASR—0
Investigating Confidence Estimation Measures for Speaker Diarization—0
Blending LLMs into Cascaded Speech Translation: KIT's Offline Speech Translation System for IWSLT 2024—0
Decoder-only Architecture for Streaming End-to-end Speech Recognition—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