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 5751–5800 of 6433 papers

TitleStatusHype
Assessing the Tolerance of Neural Machine Translation Systems Against Speech Recognition Errors—0
Assessment of ESL Learners' Syntactic Competence Based on Similarity Measures—0
Assessment of Non-native Prosody for Spanish as L2 using quantitative scores and perceptual evaluation—0
ASTER: Automatic Speech Recognition System Accessibility Testing for Stutterers—0
ASTRA: Aligning Speech and Text Representations for Asr without Sampling—0
A Streaming End-to-End Framework For Spoken Language Understanding—0
CUSIDE-array: A Streaming Multi-Channel End-to-End Speech Recognition System with Realistic Evaluations—0
Multi-Sentence Grounding for Long-term Instructional Video—0
A Study into Pre-training Strategies for Spoken Language Understanding on Dysarthric Speech—0
A Study of All-Convolutional Encoders for Connectionist Temporal Classification—0
A Study of BFLOAT16 for Deep Learning Training—0
A Study of Enhancement, Augmentation, and Autoencoder Methods for Domain Adaptation in Distant Speech Recognition—0
A Study of Gender Impact in Self-supervised Models for Speech-to-Text Systems—0
A Study of Language Modeling for Chinese Spelling Check—0
A study of latent monotonic attention variants—0
A Study of Non-autoregressive Model for Sequence Generation—0
A Study on Lip Localization Techniques used for Lip reading from a Video—0
A study on native American English speech recognition by Indian listeners with varying word familiarity level—0
表示法學習技術於節錄式語音文件摘要之研究(A Study on Representation Learning Techniques for Extractive Spoken Document Summarization) [In Chinese]—0
A Study on the Integration of Pipeline and E2E SLU systems for Spoken Semantic Parsing toward STOP Quality Challenge—0
A Study on the Integration of Pre-trained SSL, ASR, LM and SLU Models for Spoken Language Understanding—0
A study on the impact of Self-Supervised Learning on automatic dysarthric speech assessment—0
A Study on Zero-shot Non-intrusive Speech Assessment using Large Language Models—0
A Subband-Based SVM Front-End for Robust ASR—0
A Supervised STDP-based Training Algorithm for Living Neural Networks—0
A Survey of Multilingual Models for Automatic Speech Recognition—0
A Survey of the Recent Architectures of Deep Convolutional Neural Networks—0
Towards a Robust Deep Neural Network in Texts: A Survey—0
A Survey on Deep Learning Hardware Accelerators for Heterogeneous HPC Platforms—0
A Survey on Evolutionary Neural Architecture Search—0
A Survey on Methods and Theories of Quantized Neural Networks—0
A Survey on Paralinguistics in Tamil Speech Processing—0
A survey on recently proposed activation functions for Deep Learning—0
A Survey on Speech Large Language Models—0
A Survey on Spoken Italian Datasets and Corpora—0
From S4 to Mamba: A Comprehensive Survey on Structured State Space Models—0
A Swiss German Dictionary: Variation in Speech and Writing—0
Asynchronous Decentralized Distributed Training of Acoustic Models—0
Asynchronous Tool Usage for Real-Time Agents—0
A Synergistic Framework of Nonlinear Acoustic Computing and Reinforcement Learning for Real-World Human-Robot Interaction—0
A Systematic Approach to Derive a Refined Speech Corpus for Sinhala—0
ATC-ANNO: Semantic Annotation for Air Traffic Control with Assistive Auto-Annotation—0
ATCSpeech: a multilingual pilot-controller speech corpus from real Air Traffic Control environment—0
ATCSpeechNet: A multilingual end-to-end speech recognition framework for air traffic control systems—0
A Temporal Simulator for Developing Turn-Taking Methods for Spoken Dialogue Systems—0
A Text Normalisation System for Non-Standard English Words—0
A Text-to-Speech Pipeline, Evaluation Methodology, and Initial Fine-Tuning Results for Child Speech Synthesis—0
A three-dimensional approach to Visual Speech Recognition using Discrete Cosine Transforms—0
A Token-Wise Beam Search Algorithm for RNN-T—0
A Toolkit for Joint Speaker Diarization and Identification with Application to Speaker-Attributed ASR—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