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 63016350 of 6433 papers

TitleStatusHype
Connecting Humanities and Social Sciences: Applying Language and Speech Technology to Online Panel Surveys0
Connecting Speech Encoder and Large Language Model for ASR0
Consensus-based Distributed Quantum Kernel Learning for Speech Recognition0
Considerations for Ethical Speech Recognition Datasets0
Consistency Based Unsupervised Self-training For ASR Personalisation0
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
Content-Aware Speaker Embeddings for Speaker Diarisation0
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
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
Contextual Biasing of Language Models for Speech Recognition in Goal-Oriented Conversational Agents0
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
Contextualized Streaming End-to-End Speech Recognition with Trie-Based Deep Biasing and Shallow Fusion0
Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model0
Contextual Language Model Adaptation for Conversational Agents0
Contextual Metric Meta-Evaluation by Measuring Local Metric Accuracy0
Contextual RNN-T For Open Domain ASR0
Contextual Semi-Supervised Learning: An Approach To Leverage Air-Surveillance and Untranscribed ATC Data in ASR Systems0
Contextual Speech Recognition with Difficult Negative Training Examples0
Contextual-Utterance Training for Automatic Speech Recognition0
Continual Learning for End-to-End ASR by Averaging Domain Experts0
Continual Learning for On-Device Speech Recognition using Disentangled Conformers0
Continual Learning in Machine Speech Chain Using Gradient Episodic Memory0
Continual learning using lattice-free MMI for speech recognition0
Towards continually learning new languages0
An Adapter Based Pre-Training for Efficient and Scalable Self-Supervised Speech Representation Learning0
Continued Pretraining for Domain Adaptation of Wav2vec2.0 in Automatic Speech Recognition for Elementary Math Classroom Settings0
Continuous Expressive Speaking Styles Synthesis based on CVSM and MR-HMM0
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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