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 18511900 of 6433 papers

TitleStatusHype
Direction-Aware Joint Adaptation of Neural Speech Enhancement and Recognition in Real Multiparty Conversational Environments0
Automatic Speech Recognition in German: A Detailed Error Analysis0
Classifying Arab Names Geographically0
Direct Speech to Speech Translation: A Review0
A Real-time Robot-based Auxiliary System for Risk Evaluation of COVID-19 Infection0
Classification of Closely Related Sub-dialects of Arabic Using Support-Vector Machines0
Discourse-Based Modeling for AAC0
Discourse on ASR Measurement: Introducing the ARPOCA Assessment Tool0
Discovering Canonical Indian English Accents: A Crowdsourcing-based Approach0
Discovering Latent Structure in Task-Oriented Dialogues0
Automatic Speech Recognition of Non-Native Child Speech for Language Learning Applications0
Discovery of Important Subsequences in Electrocardiogram Beats Using the Nearest Neighbour Algorithm0
Classification Error Bound for Low Bayes Error Conditions in Machine Learning0
Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition0
Automatic Speech Recognition System-Independent Word Error Rate Estimation0
Discrete Multimodal Transformers with a Pretrained Large Language Model for Mixed-Supervision Speech Processing0
Automatic Speech Recognition using Advanced Deep Learning Approaches: A survey0
Discriminative Acoustic Word Embeddings: Recurrent Neural Network-Based Approaches0
Discriminative Joint Modeling of Lexical Variation and Acoustic Confusion for Automated Narrative Retelling Assessment0
A Real-life, French-accented Corpus of Air Traffic Control Communications0
Accented Speech Recognition Inspired by Human Perception0
Discriminative Segmental Cascades for Feature-Rich Phone Recognition0
Discriminative Self-training for Punctuation Prediction0
Discriminative Speech Recognition Rescoring with Pre-trained Language Models0
Discriminative state tracking for spoken dialog systems0
Discriminative training of RNNLMs with the average word error criterion0
Disentangled Speech Representation Learning Based on Factorized Hierarchical Variational Autoencoder with Self-Supervised Objective0
Disentangled-Transformer: An Explainable End-to-End Automatic Speech Recognition Model with Speech Content-Context Separation0
Disentangleing Content and Fine-grained Prosody Information via Hybrid ASR Bottleneck Features for Voice Conversion0
Disentangling Prosody Representations with Unsupervised Speech Reconstruction0
Class-based LSTM Russian Language Model with Linguistic Information0
Automatic Spoken Language Identification using a Time-Delay Neural Network0
Disfluency Correction using Unsupervised and Semi-supervised Learning0
Disfluency Detection with Unlabeled Data and Small BERT Models0
DisfluencyFixer: A tool to enhance Language Learning through Speech To Speech Disfluency Correction0
Dissecting User-Perceived Latency of On-Device E2E Speech Recognition0
Distillation Strategies for Discriminative Speech Recognition Rescoring0
Class-Based Language Modeling for Translating into Morphologically Rich Languages0
Distilling HuBERT with LSTMs via Decoupled Knowledge Distillation0
A Random Gossip BMUF Process for Neural Language Modeling0
Distilling Knowledge Using Parallel Data for Far-field Speech Recognition0
Distilling the Knowledge of BERT for CTC-based ASR0
Citrinet: Closing the Gap between Non-Autoregressive and Autoregressive End-to-End Models for Automatic Speech Recognition0
DistillW2V2: A Small and Streaming Wav2vec 2.0 Based ASR Model0
Adversarial synthesis based data-augmentation for code-switched spoken language identification0
2020福爾摩沙臺語語音辨識比賽之初步實驗 (A Preliminary Study of Formosa Speech Recognition Challenge 2020 – Taiwanese ASR)0
Dropout: A Simple Way to Prevent Neural Networks from Overfitting0
Distinguishing Common and Proper Nouns0
Distributed Deep Learning Strategies For Automatic Speech Recognition0
CIF-based Collaborative Decoding for End-to-end Contextual Speech Recognition0
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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