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 2851–2900 of 6433 papers

TitleStatusHype
Fast Real-time Personalized Speech Enhancement: End-to-End Enhancement Network (E3Net) and Knowledge Distillation—0
End-to-end model for named entity recognition from speech without paired training data—0
InterAug: Augmenting Noisy Intermediate Predictions for CTC-based ASR—0
Alternate Intermediate Conditioning with Syllable-level and Character-level Targets for Japanese ASR—0
Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding—0
Probing Speech Emotion Recognition Transformers for Linguistic Knowledge—0
Text-To-Speech Data Augmentation for Low Resource Speech Recognition—0
Filter-based Discriminative Autoencoders for Children Speech Recognition—0
End-to-end multi-talker audio-visual ASR using an active speaker attention module—0
End-to-End Multi-speaker ASR with Independent Vector Analysis—0
End-to-End Integration of Speech Recognition, Speech Enhancement, and Self-Supervised Learning Representation—0
Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition—0
Multiple Confidence Gates For Joint Training Of SE And ASR—0
Effectiveness of text to speech pseudo labels for forced alignment and cross lingual pretrained models for low resource speech recognition—0
An Empirical Study of Language Model Integration for Transducer based Speech Recognition—0
Analyzing the factors affecting usefulness of Self-Supervised Pre-trained Representations for Speech Recognition—0
A Comparative Study on Speaker-attributed Automatic Speech Recognition in Multi-party Meetings—0
Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset—0
CUSIDE: Chunking, Simulating Future Context and Decoding for Streaming ASR—0
Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data—0
Importance of Different Temporal Modulations of Speech: A Tale of Two Perspectives—0
Improving Language Identification of Accented Speech—0
Memory-Efficient Training of RNN-Transducer with Sampled Softmax—0
Exploiting Single-Channel Speech for Multi-Channel End-to-End Speech Recognition: A Comparative Study—0
Is Word Error Rate a good evaluation metric for Speech Recognition in Indic Languages?—0
Code Switched and Code Mixed Speech Recognition for Indic languages—0
Federated Domain Adaptation for ASR with Full Self-Supervision—0
Using Adapters to Overcome Catastrophic Forgetting in End-to-End Automatic Speech RecognitionCode0
Improving Speech Recognition for Indic Languages using Language Model—0
Dynamic Latency for CTC-Based Streaming Automatic Speech Recognition With Emformer—0
Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data—0
Analysis of EEG frequency bands for Envisioned Speech RecognitionCode0
Streaming parallel transducer beam search with fast-slow cascaded encoders—0
Short-Term Word-Learning in a Dynamically Changing Environment—0
Frequency-Directional Attention Model for Multilingual Automatic Speech Recognition—0
Shallow Fusion of Weighted Finite-State Transducer and Language Model for Text NormalizationCode0
Improving Generalization of Deep Neural Network Acoustic Models with Length Perturbation and N-best Based Label Smoothing—0
Mel Frequency Spectral Domain Defenses against Adversarial Attacks on Speech Recognition Systems—0
Finnish Parliament ASR corpus - Analysis, benchmarks and statisticsCode0
On-the-Fly Feature Based Rapid Speaker Adaptation for Dysarthric and Elderly Speech Recognition—0
A Dataset for Speech Emotion Recognition in Greek Theatrical PlaysCode0
A Speech Representation Anonymization Framework via Selective Noise PerturbationCode0
Impact of Dataset on Acoustic Models for Automatic Speech Recognition—0
Chain-based Discriminative Autoencoders for Speech Recognition—0
Speech-enhanced and Noise-aware Networks for Robust Speech RecognitionCode0
Disentangleing Content and Fine-grained Prosody Information via Hybrid ASR Bottleneck Features for Voice Conversion—0
Computing Optimal Location of Microphone for Improved Speech Recognition—0
Lahjoita puhetta -- a large-scale corpus of spoken Finnish with some benchmarks—0
A Text-to-Speech Pipeline, Evaluation Methodology, and Initial Fine-Tuning Results for Child Speech Synthesis—0
Pseudo Label Is Better Than Human Label—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