1.1k citations
- Amazon (United States)US32 papers
- Carnegie Mellon UniversityUS8 papers
- Massachusetts Institute of TechnologyUS8 papers
- Microsoft Research (United Kingdom)GB8 papers
- Google (United States)US7 papers
- Johns Hopkins UniversityUS7 papers
- Meta (Israel)IL6 papers
- University of Maryland, College ParkUS6 papers
- California Southern UniversityUS5 papers
- University of California, Los AngelesUS5 papers
- University of OxfordGB5 papers
- University of Southern CaliforniaUS5 papers
21 papers · 1 filter
Improving the expressiveness of neural vocoding with non-affine Normalizing Flows
Adam Gabryś, Yunlong Jiao, Viacheslav Klimkov +2
This paper proposes a general enhancement to the Normalizing Flows (NF) used in neural vocoding. As a case study, we improve expressive speech vocoding with a revamped Parallel Wav…
Listen with Intent: Improving Speech Recognition with Audio-to-Intent Front-End
Swayambhu Nath Ray, Minhua Wu, Anirudh Raju +7
Comprehending the overall intent of an utterance helps a listener recognize the individual words spoken. Inspired by this fact, we perform a novel study of the impact of explicitly…
End-to-End Multi-Channel Transformer for Speech Recognition
Feng-Ju Chang, Martin Radfar, Athanasios Mouchtaris +2
Transformers are powerful neural architectures that allow integrating different modalities using attention mechanisms. In this paper, we leverage the neural transformer architectur…
Streaming Multi-speaker ASR with RNN-T
Ilya Sklyar, Anna Piunova, Yulan Liu
Recent research shows end-to-end ASR systems can recognize overlapped speech from multiple speakers. However, all published works have assumed no latency constraints during inferen…
On Front-end Gain Invariant Modeling for Wake Word Spotting
Yixin Gao, Noah D. Stein, Chieh-Chi Kao +4
Wake word (WW) spotting is challenging in far-field due to the complexities and variations in acoustic conditions and the environmental interference in signal transmission. A suite…
Improving Device Directedness Classification of Utterances with Semantic Lexical Features
Kellen Gillespie, Ioannis C. Konstantakopoulos, Xingzhi Guo +2
User interactions with personal assistants like Alexa, Google Home and Siri are typically initiated by a wake term or wakeword. Several personal assistants feature "follow-up" mode…