66 citations · 141 across the 16 of their papers we have counts for
4 papers · 1 filter
Transformer-Transducer: End-to-End Speech Recognition with Self-Attention
Ching-Feng Yeh, Jay Mahadeokar, Kaustubh Kalgaonkar +6
We explore options to use Transformer networks in neural transducer for end-to-end speech recognition. Transformer networks use self-attention for sequence modeling and comes with…
From Senones to Chenones: Tied Context-Dependent Graphemes for Hybrid Speech Recognition
Duc Le, Xiaohui Zhang, Weiyi Zheng +3
There is an implicit assumption that traditional hybrid approaches for automatic speech recognition (ASR) cannot directly model graphemes and need to rely on phonetic lexicons to g…
G2G: TTS-Driven Pronunciation Learning for Graphemic Hybrid ASR
Duc Le, Thilo Koehler, Christian Fuegen +1
Grapheme-based acoustic modeling has recently been shown to outperform phoneme-based approaches in both hybrid and end-to-end automatic speech recognition (ASR), even on non-phonem…
Transformer-based Acoustic Modeling for Hybrid Speech Recognition
Yongqiang Wang, Abdelrahman Mohamed, Duc Le +10
We propose and evaluate transformer-based acoustic models (AMs) for hybrid speech recognition. Several modeling choices are discussed in this work, including various positional emb…