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 25512600 of 6433 papers

TitleStatusHype
End-to-end model for named entity recognition from speech without paired training data0
Text-To-Speech Data Augmentation for Low Resource Speech Recognition0
PriMock57: A Dataset Of Primary Care Mock ConsultationsCode1
Multiple Confidence Gates For Joint Training Of SE And ASR0
Alternate Intermediate Conditioning with Syllable-level and Character-level Targets for Japanese ASR0
InterAug: Augmenting Noisy Intermediate Predictions for CTC-based ASR0
Multi-task RNN-T with Semantic Decoder for Streamable Spoken Language Understanding0
End-to-end multi-talker audio-visual ASR using an active speaker attention module0
End-to-End Multi-speaker ASR with Independent Vector Analysis0
End-to-End Integration of Speech Recognition, Speech Enhancement, and Self-Supervised Learning Representation0
Zero-Shot Cross-lingual Aphasia Detection using Automatic Speech Recognition0
Probing Speech Emotion Recognition Transformers for Linguistic Knowledge0
Filter-based Discriminative Autoencoders for Children Speech Recognition0
Importance of Different Temporal Modulations of Speech: A Tale of Two Perspectives0
Effectiveness of text to speech pseudo labels for forced alignment and cross lingual pretrained models for low resource speech recognition0
An Empirical Study of Language Model Integration for Transducer based Speech Recognition0
Analyzing the factors affecting usefulness of Self-Supervised Pre-trained Representations for Speech Recognition0
A Comparative Study on Speaker-attributed Automatic Speech Recognition in Multi-party Meetings0
CUSIDE: Chunking, Simulating Future Context and Decoding for Streaming ASR0
A Hybrid Continuity Loss to Reduce Over-Suppression for Time-domain Target Speaker ExtractionCode1
Pre-Training Transformer Decoder for End-to-End ASR Model with Unpaired Speech Data0
How Does Pre-trained Wav2Vec 2.0 Perform on Domain Shifted ASR? An Extensive Benchmark on Air Traffic Control CommunicationsCode1
Improving Language Identification of Accented Speech0
Exploiting Single-Channel Speech for Multi-Channel End-to-End Speech Recognition: A Comparative Study0
Memory-Efficient Training of RNN-Transducer with Sampled Softmax0
HiFi-VC: High Quality ASR-Based Voice ConversionCode1
Open Source MagicData-RAMC: A Rich Annotated Mandarin Conversational(RAMC) Speech Dataset0
indic-punct: An automatic punctuation restoration and inverse text normalization framework for Indic languagesCode1
Using Adapters to Overcome Catastrophic Forgetting in End-to-End Automatic Speech RecognitionCode0
Is Word Error Rate a good evaluation metric for Speech Recognition in Indic Languages?0
Improving Speech Recognition for Indic Languages using Language Model0
Vakyansh: ASR Toolkit for Low Resource Indic languagesCode2
Streaming Speaker-Attributed ASR with Token-Level Speaker EmbeddingsCode1
Code Switched and Code Mixed Speech Recognition for Indic languages0
Federated Domain Adaptation for ASR with Full Self-Supervision0
ASR data augmentation in low-resource settings using cross-lingual multi-speaker TTS and cross-lingual voice conversionCode1
Recent improvements of ASR models in the face of adversarial attacksCode1
4-bit Conformer with Native Quantization Aware Training for Speech RecognitionCode2
Shallow Fusion of Weighted Finite-State Transducer and Language Model for Text NormalizationCode0
Earnings-22: A Practical Benchmark for Accents in the WildCode1
Improving Generalization of Deep Neural Network Acoustic Models with Length Perturbation and N-best Based Label Smoothing0
Dynamic Latency for CTC-Based Streaming Automatic Speech Recognition With Emformer0
Integrating Lattice-Free MMI into End-to-End Speech RecognitionCode1
Noise-robust Speech Recognition with 10 Minutes Unparalleled In-domain Data0
Mel Frequency Spectral Domain Defenses against Adversarial Attacks on Speech Recognition Systems0
Short-Term Word-Learning in a Dynamically Changing Environment0
Analysis of EEG frequency bands for Envisioned Speech RecognitionCode0
Streaming parallel transducer beam search with fast-slow cascaded encoders0
Unsupervised Text-to-Speech Synthesis by Unsupervised Automatic Speech RecognitionCode1
Frequency-Directional Attention Model for Multilingual Automatic 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