Switchboard
Canonical32papers using it
2021first seen
The Switchboard dataset is an unlabeled target domain used to evaluate the performance of speech recognition systems, particularly in the context of unsupervised domain adaptation.
Papers using Switchboard (32)
- Non-Autoregressive Minimum Bayes' Risk Decoding for Fast Speech RecognitionTeaching the Teachers: Boosting unsupervised domain adaptation in speech recognition by ensemble updateChain-of-Thought Training for Open E2E Spoken Dialogue SystemsTowards One-bit ASR: Extremely Low-bit Conformer Quantization Using Co-training and Stochastic PrecisionAdapting GPT, GPT-2 and BERT Language Models for Speech RecognitionNIST SRE CTS Superset: A large-scale dataset for telephony speaker
recognitionEfficient Training of Neural Transducer for Speech RecognitionAdaptable End-to-End ASR Models using Replaceable Internal LMs and
Residual SoftmaxAdvancing CTC-CRF Based End-to-End Speech Recognition with Wordpieces
and ConformersToward Zero Oracle Word Error Rate on the Switchboard BenchmarkSvarah: Evaluating English ASR Systems on Indian AccentsProgressive unsupervised domain adaptation for ASR using ensemble models
and multi-stage trainingImproving Confidence Estimation on Out-of-Domain Data for End-to-End
Speech RecognitionASR4REAL: An extended benchmark for speech modelsHMM vs. CTC for Automatic Speech Recognition: Comparison Based on
Full-Sum Training from ScratchDecoder-only Architecture for Speech Recognition with CTC Prompts and
Text Data AugmentationDual Causal/Non-Causal Self-Attention for Streaming End-to-End Speech
RecognitionAutomatic Learning of Subword Dependent Model ScalesIntegrating Text Inputs For Training and Adapting RNN Transducer ASR
ModelsGeneration of Speaker Representations Using Heterogeneous Training Batch
AssemblyPADA: Pruning Assisted Domain Adaptation for Self-Supervised Speech
RepresentationsTwo-pass Decoding and Cross-adaptation Based System Combination of
End-to-end Conformer and Hybrid TDNN ASR SystemsConfidence Score Based Conformer Speaker Adaptation for Speech
RecognitionCCC-wav2vec 2.0: Clustering aided Cross Contrastive Self-supervised
learning of speech representationsStreaming Joint Speech Recognition and Disfluency DetectionSpeaker Adaptation for End-To-End Speech Recognition Systems in Noisy
EnvironmentsUnsupervised Model-based speaker adaptation of end-to-end lattice-free
MMI model for speech recognitionAnalyzing And Improving Neural Speaker Embeddings for ASRSemi-Autoregressive Streaming ASR With Label ContextConnecting Speech Encoder and Large Language Model for ASRAutomatic Speech Recognition System-Independent Word Error Rate
EstimationImproving Speech Recognition Error Prediction for Modern and
Off-the-shelf Speech Recognizers