19 citations · 40 across the 10 of their papers we have counts for
10 papers
Multi-Stage Multi-Modal Pre-Training for Automatic Speech Recognition
Yash Jain, David Chan, Pranav Dheram +4
Recent advances in machine learning have demonstrated that multi-modal pre-training can improve automatic speech recognition (ASR) performance compared to randomly initialized mode…
Turn-taking and Backchannel Prediction with Acoustic and Large Language Model Fusion
Jinhan Wang, Long Chen, Aparna Khare +6
We propose an approach for continuous prediction of turn-taking and backchanneling locations in spoken dialogue by fusing a neural acoustic model with a large language model (LLM).…
Two-pass Endpoint Detection for Speech Recognition
Anirudh Raju, Aparna Khare, Di He +9
Endpoint (EP) detection is a key component of far-field speech recognition systems that assist the user through voice commands. The endpoint detector has to trade-off between accur…
Multimodal Attention Merging for Improved Speech Recognition and Audio Event Classification
Anirudh S. Sundar, Chao-Han Huck Yang, David M. Chan +3
Training large foundation models using self-supervised objectives on unlabeled data, followed by fine-tuning on downstream tasks, has emerged as a standard procedure. Unfortunately…
Cross-utterance ASR Rescoring with Graph-based Label Propagation
Srinath Tankasala, Long Chen, Andreas Stolcke +6
We propose a novel approach for ASR N-best hypothesis rescoring with graph-based label propagation by leveraging cross-utterance acoustic similarity. In contrast to conventional ne…
Adaptive Endpointing with Deep Contextual Multi-armed Bandits
Do June Min, Andreas Stolcke, Anirudh Raju +4
Current endpointing (EP) solutions learn in a supervised framework, which does not allow the model to incorporate feedback and improve in an online setting. Also, it is a common pr…
Stutter-TTS: Controlled Synthesis and Improved Recognition of Stuttered Speech
Xin Zhang, Iván Vallés-Pérez, Andreas Stolcke +5
Stuttering is a speech disorder where the natural flow of speech is interrupted by blocks, repetitions or prolongations of syllables, words and phrases. The majority of existing au…
Graph-based Multi-View Fusion and Local Adaptation: Mitigating Within-Household Confusability for Speaker Identification
Long Chen, Yixiong Meng, Venkatesh Ravichandran +1
Speaker identification (SID) in the household scenario (e.g., for smart speakers) is an important but challenging problem due to limited number of labeled (enrollment) utterances,…
Enhancing ASR for Stuttered Speech with Limited Data Using Detect and Pass
Olabanji Shonibare, Xiaosu Tong, Venkatesh Ravichandran
It is estimated that around 70 million people worldwide are affected by a speech disorder called stuttering. With recent advances in Automatic Speech Recognition (ASR), voice assis…
Graph-based Label Propagation for Semi-Supervised Speaker Identification
Long Chen, Venkatesh Ravichandran, Andreas Stolcke
Speaker identification in the household scenario (e.g., for smart speakers) is typically based on only a few enrollment utterances but a much larger set of unlabeled data, suggesti…