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 14011450 of 6433 papers

TitleStatusHype
Conformer-Based Speech Recognition On Extreme Edge-Computing Devices0
ASR error management for improving spoken language understanding0
Coarse-To-Fine And Cross-Lingual ASR Transfer0
Conformer LLMs -- Convolution Augmented Large Language Models0
Conformers are All You Need for Visual Speech Recognition0
ASR for Documenting Acutely Under-Resourced Indigenous Languages0
CoALT: A Software for Comparing Automatic Labelling Tools0
Confusion Network for Arabic Name Disambiguation and Transliteration in Statistical Machine Translation0
Connecting Humanities and Social Sciences: Applying Language and Speech Technology to Online Panel Surveys0
Connecting Speech Encoder and Large Language Model for ASR0
ASR in German: A Detailed Error Analysis0
A Human Digital Twin Architecture for Knowledge-based Interactions and Context-Aware Conversations0
Consensus-based Distributed Quantum Kernel Learning for Speech Recognition0
Considerations for Ethical Speech Recognition Datasets0
A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search0
CNVSRC 2024: The Second Chinese Continuous Visual Speech Recognition Challenge0
Assessing ASR Model Quality on Disordered Speech using BERTScore0
Constrained Output Embeddings for End-to-End Code-Switching Speech Recognition with Only Monolingual Data0
Constrained Variational Autoencoder for improving EEG based Speech Recognition Systems0
Constructing Effective Machine Learning Models for the Sciences: A Multidisciplinary Perspective0
Constructing Long Short-Term Memory based Deep Recurrent Neural Networks for Large Vocabulary Speech Recognition0
Construction of a Large-scale Japanese ASR Corpus on TV Recordings0
Construction of English-French Multimodal Affective Conversational Corpus from TV Dramas0
Constructive Interaction for Talking about Interesting Topics0
Acoustic-to-Word Recognition with Sequence-to-Sequence Models0
Content-Aware Speaker Embeddings for Speaker Diarisation0
CNVSRC 2023: The First Chinese Continuous Visual Speech Recognition Challenge0
Content-Context Factorized Representations for Automated Speech Recognition0
Context-aware Fine-tuning of Self-supervised Speech Models0
Context-aware Neural-based Dialog Act Classification on Automatically Generated Transcriptions0
Context-Aware Selective Label Smoothing for Calibrating Sequence Recognition Model0
Context-Aware Transformer Transducer for Speech Recognition0
Context-based out-of-vocabulary word recovery for ASR systems in Indian languages0
Context-Dependent Acoustic Modeling without Explicit Phone Clustering0
A Review of Meta-Reinforcement Learning for Deep Neural Networks Architecture Search0
Context-sensitive evaluation of automatic speech recognition: considering user experience & language variation0
Contextual Adapters for Personalized Speech Recognition in Neural Transducers0
Contextual ASR Error Handling with LLMs Augmentation for Goal-Oriented Conversational AI0
A Fast-Converged Acoustic Modeling for Korean Speech Recognition: A Preliminary Study on Time Delay Neural Network0
Contextual Biasing of Named-Entities with Large Language Models0
Contextual Biasing to Improve Domain-specific Custom Vocabulary Audio Transcription without Explicit Fine-Tuning of Whisper Model0
Contextual Biasing with the Knuth-Morris-Pratt Matching Algorithm0
Contextual Dependencies in Time-Continuous Multidimensional Affect Recognition0
Contextualization of ASR with LLM using phonetic retrieval-based augmentation0
Contextualized Automatic Speech Recognition with Attention-Based Bias Phrase Boosted Beam Search0
Contextualized Automatic Speech Recognition with Dynamic Vocabulary0
Contextualized Automatic Speech Recognition with Dynamic Vocabulary Prediction and Activation0
Contextualized End-to-end Automatic Speech Recognition with Intermediate Biasing Loss0
Contextualized End-to-End Speech Recognition with Contextual Phrase Prediction Network0
AccentFold: A Journey through African Accents for Zero-Shot ASR Adaptation to Target Accents0
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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
9HMM-TDNN + iVectorsWord Error Rate (WER)4.8Unverified
10Gated ConvNetsWord 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
7DNN MPEPercentage error12.9Unverified
8DNN MMIPercentage error12.9Unverified
9HMM-TDNN + pNorm + speed up/down speechPercentage error12.9Unverified
10HMM-DNN +sMBRPercentage 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
6Convolutional Speech RecognitionWord Error Rate (WER)3.5Unverified
7TC-DNN-BLSTM-DNNWord 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