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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 5001–5050 of 6433 papers

TitleStatusHype
Pruned RNN-T for fast, memory-efficient ASR training—0
Pseudo-Labeling for Massively Multilingual Speech Recognition—0
Pseudo Label Is Better Than Human Label—0
PSRB: A Comprehensive Benchmark for Evaluating Persian ASR Systems—0
Punctuation Prediction for Polish Texts using Transformers—0
Punctuation Prediction in Spontaneous Conversations: Can We Mitigate ASR Errors with Retrofitted Word Embeddings?—0
Punctuation Prediction with Transition-based Parsing—0
SemEval 2022 Task 12: Symlink- Linking Mathematical Symbols to their Descriptions—0
Punctuation Restoration in Spanish Customer Support Transcripts using Transfer Learning—0
Purely sequence-trained neural networks for ASR based on lattice-free MMI—0
Pushing the boundaries of audiovisual word recognition using Residual Networks and LSTMs—0
Pushing the Limits of Non-Autoregressive Speech Recognition—0
PyDial: A Multi-domain Statistical Dialogue System Toolkit—0
Pynini: A Python library for weighted finite-state grammar compilation—0
PyOpenDial: A Python-based Domain-Independent Toolkit for Developing Spoken Dialogue Systems with Probabilistic Rules—0
Pyramid Multi-branch Fusion DCNN with Multi-Head Self-Attention for Mandarin Speech Recognition—0
運用Python結合語音辨識及合成技術於自動化音文同步之實作(A Python Implementation of Automatic Speech-text Synchronization Using Speech Recognition and Text-to-Speech Technology)[In Chinese]—0
QASR: QCRI Aljazeera Speech Resource -- A Large Scale Annotated Arabic Speech Corpus—0
QASR: QCRI Aljazeera Speech Resource A Large Scale Annotated Arabic Speech Corpus—0
QCRI Live Speech Translation System—0
Qieemo: Speech Is All You Need in the Emotion Recognition in Conversations—0
Qifusion-Net: Layer-adapted Stream/Non-stream Model for End-to-End Multi-Accent Speech Recognition—0
Qualitative investigation of the display of speech recognition results for communication with deaf people—0
Quality Estimation for Automatic Speech Recognition—0
Quantification of stylistic differences in human- and ASR-produced transcripts of African American English—0
Quantifying and Maximizing the Benefits of Back-End Noise Adaption on Attention-Based Speech Recognition Models—0
Quantifying the Dialect Gap and its Correlates Across Languages—0
Quantifying the Role of Textual Predictability in Automatic Speech Recognition—0
Quantitative Analysis of Audio-Visual Tasks: An Information-Theoretic Perspective—0
Quantitative Analysis of Image Classification Techniques for Memory-Constrained Devices—0
Quantization of Acoustic Model Parameters in Automatic Speech Recognition Framework—0
Quantization of Deep Neural Networks for Accurate Edge Computing—0
Quantized Approximate Signal Processing (QASP): Towards Homomorphic Encryption for audio—0
Quaternion Neural Networks for Multi-channel Distant Speech Recognition—0
Query-by-example on-device keyword spotting—0
QuEst - A translation quality estimation framework—0
Quran Recitation Recognition using End-to-End Deep Learning—0
Qwen vs. Gemma Integration with Whisper: A Comparative Study in Multilingual SpeechLLM Systems—0
RACAI Entry for the IWSLT 2016 Shared Task—0
RADIA -- Radio Advertisement Detection with Intelligent Analytics—0
Radio2Text: Streaming Speech Recognition Using mmWave Radio Signals—0
RAND: Robustness Aware Norm Decay For Quantized Seq2seq Models—0
Rapid Language Adaptation for Multilingual E2E Speech Recognition Using Encoder Prompting—0
Rapidly Testing the Interaction Model of a Pronunciation Training System via Wizard-of-Oz—0
RAPIDNN: In-Memory Deep Neural Network Acceleration Framework—0
RASR2: The RWTH ASR Toolkit for Generic Sequence-to-sequence Speech Recognition—0
Rate-Invariant Analysis of Trajectories on Riemannian Manifolds with Application in Visual Speech Recognition—0
Rationalizing Predictions by Adversarial Information Calibration—0
Raw Waveform Encoder with Multi-Scale Globally Attentive Locally Recurrent Networks for End-to-End Speech Recognition—0
Readability Assessment of Translated Texts—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