4 citations · 23 across the 41 of their papers we have counts for
11 papers · 1 filter
4D ASR: Joint modeling of CTC, Attention, Transducer, and Mask-Predict decoders
Yui Sudo, Muhammad Shakeel, Brian Yan +2
The network architecture of end-to-end (E2E) automatic speech recognition (ASR) can be classified into several models, including connectionist temporal classification (CTC), recurr…
Align, Write, Re-order: Explainable End-to-End Speech Translation via Operation Sequence Generation
Motoi Omachi, Brian Yan, Siddharth Dalmia +2
The black-box nature of end-to-end speech translation (E2E ST) systems makes it difficult to understand how source language inputs are being mapped to the target language. To solve…
Towards Zero-Shot Code-Switched Speech Recognition
Brian Yan, Matthew Wiesner, Ondrej Klejch +2
In this work, we seek to build effective code-switched (CS) automatic speech recognition systems (ASR) under the zero-shot setting where no transcribed CS speech data is available…
Avoid Overthinking in Self-Supervised Models for Speech Recognition
Dan Berrebbi, Brian Yan, Shinji Watanabe
Self-supervised learning (SSL) models reshaped our approach to speech, language and vision. However their huge size and the opaque relations between their layers and tasks result i…
Token-level Sequence Labeling for Spoken Language Understanding using Compositional End-to-End Models
Siddhant Arora, Siddharth Dalmia, Brian Yan +3
End-to-end spoken language understanding (SLU) systems are gaining popularity over cascaded approaches due to their simplicity and ability to avoid error propagation. However, thes…
CTC Alignments Improve Autoregressive Translation
Brian Yan, Siddharth Dalmia, Yosuke Higuchi +4
Connectionist Temporal Classification (CTC) is a widely used approach for automatic speech recognition (ASR) that performs conditionally independent monotonic alignment. However fo…
BERT Meets CTC: New Formulation of End-to-End Speech Recognition with Pre-trained Masked Language Model
Yosuke Higuchi, Brian Yan, Siddhant Arora +3
This paper presents BERT-CTC, a novel formulation of end-to-end speech recognition that adapts BERT for connectionist temporal classification (CTC). Our formulation relaxes the con…
Bayes risk CTC: Controllable CTC alignment in Sequence-to-Sequence tasks
Jinchuan Tian, Brian Yan, Jianwei Yu +3
Sequence-to-Sequence (seq2seq) tasks transcribe the input sequence to a target sequence. The Connectionist Temporal Classification (CTC) criterion is widely used in multiple seq2se…
Two-Pass Low Latency End-to-End Spoken Language Understanding
Siddhant Arora, Siddharth Dalmia, Xuankai Chang +3
End-to-end (E2E) models are becoming increasingly popular for spoken language understanding (SLU) systems and are beginning to achieve competitive performance to pipeline-based app…
ESPnet-SE++: Speech Enhancement for Robust Speech Recognition, Translation, and Understanding
Yen-Ju Lu, Xuankai Chang, Chenda Li +10
This paper presents recent progress on integrating speech separation and enhancement (SSE) into the ESPnet toolkit. Compared with the previous ESPnet-SE work, numerous features hav…
Combining Spectral and Self-Supervised Features for Low Resource Speech Recognition and Translation
Dan Berrebbi, Jiatong Shi, Brian Yan +3
Self-Supervised Learning (SSL) models have been successfully applied in various deep learning-based speech tasks, particularly those with a limited amount of data. However, the qua…