22 citations · 30 across the 6 of their papers we have counts for
11 papers
Analysis of Self-Attention Head Diversity for Conformer-based Automatic Speech Recognition
Kartik Audhkhasi, Yinghui Huang, Bhuvana Ramabhadran +1
Attention layers are an integral part of modern end-to-end automatic speech recognition systems, for instance as part of the Transformer or Conformer architecture. Attention is typ…
Leveraging Unpaired Text Data for Training End-to-End Speech-to-Intent Systems
Yinghui Huang, Hong-Kwang Kuo, Samuel Thomas +5
Training an end-to-end (E2E) neural network speech-to-intent (S2I) system that directly extracts intents from speech requires large amounts of intent-labeled speech data, which is…
End-to-End Spoken Language Understanding Without Full Transcripts
Hong-Kwang J. Kuo, Zoltán Tüske, Samuel Thomas +7
An essential component of spoken language understanding (SLU) is slot filling: representing the meaning of a spoken utterance using semantic entity labels. In this paper, we develo…
Single headed attention based sequence-to-sequence model for state-of-the-art results on Switchboard
Zoltán Tüske, George Saon, Kartik Audhkhasi +1
It is generally believed that direct sequence-to-sequence (seq2seq) speech recognition models are competitive with hybrid models only when a large amount of data, at least a thousa…
Challenging the Boundaries of Speech Recognition: The MALACH Corpus
Michael Picheny, Zóltan Tüske, Brian Kingsbury +3
There has been huge progress in speech recognition over the last several years. Tasks once thought extremely difficult, such as SWITCHBOARD, now approach levels of human performanc…
Guiding CTC Posterior Spike Timings for Improved Posterior Fusion and Knowledge Distillation
Gakuto Kurata, Kartik Audhkhasi
Conventional automatic speech recognition (ASR) systems trained from frame-level alignments can easily leverage posterior fusion to improve ASR accuracy and build a better single m…