11 citations · 27 across the 11 of their papers we have counts for
6 papers · 1 filter
Accent-Robust Automatic Speech Recognition Using Supervised and Unsupervised Wav2vec Embeddings
Jialu Li, Vimal Manohar, Pooja Chitkara +5
Speech recognition models often obtain degraded performance when tested on speech with unseen accents. Domain-adversarial training (DAT) and multi-task learning (MTL) are two commo…
On lattice-free boosted MMI training of HMM and CTC-based full-context ASR models
Xiaohui Zhang, Vimal Manohar, David Zhang +7
Hybrid automatic speech recognition (ASR) models are typically sequentially trained with CTC or LF-MMI criteria. However, they have vastly different legacies and are usually implem…
Benchmarking LF-MMI, CTC and RNN-T Criteria for Streaming ASR
Xiaohui Zhang, Frank Zhang, Chunxi Liu +8
In this work, to measure the accuracy and efficiency for a latency-controlled streaming automatic speech recognition (ASR) application, we perform comprehensive evaluations on thre…
Weak-Attention Suppression For Transformer Based Speech Recognition
Yangyang Shi, Yongqiang Wang, Chunyang Wu +5
Transformers, originally proposed for natural language processing (NLP) tasks, have recently achieved great success in automatic speech recognition (ASR). However, adjacent acousti…
Streaming Transformer-based Acoustic Models Using Self-attention with Augmented Memory
Chunyang Wu, Yongqiang Wang, Yangyang Shi +2
Transformer-based acoustic modeling has achieved great suc-cess for both hybrid and sequence-to-sequence speech recogni-tion. However, it requires access to the full sequence, and…
Faster, Simpler and More Accurate Hybrid ASR Systems Using Wordpieces
Frank Zhang, Yongqiang Wang, Xiaohui Zhang +3
In this work, we first show that on the widely used LibriSpeech benchmark, our transformer-based context-dependent connectionist temporal classification (CTC) system produces state…