Search Results for author:
Found 9 papers, 3 papers with code
BLSTM-Based Confidence Estimation for End-to-End Speech Recognition
Confidence estimation, in which we estimate the reliability of each recognized token (e. g., word, sub-word, and character) in automatic speech recognition (ASR) hypotheses and detect incorrectly recognized tokens, is an important function for developing ASR applications.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Lattice Rescoring Based on Large Ensemble of Complementary Neural Language Models
We investigate the effectiveness of using a large ensemble of advanced neural language models (NLMs) for lattice rescoring on automatic speech recognition (ASR) hypotheses.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Iterative Shallow Fusion of Backward Language Model for End-to-End Speech Recognition
In experiments using an attention-based encoder-decoder ASR system, we confirmed that ISF using the PBLM shows comparable performance with SF using the FLM.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+3
Discriminative Training of VBx Diarization
Bayesian HMM clustering of x-vector sequences (VBx) has become a widely adopted diarization baseline model in publications and challenges.
NTT speaker diarization system for CHiME-7: multi-domain, multi-microphone End-to-end and vector clustering diarization
This paper details our speaker diarization system designed for multi-domain, multi-microphone casual conversations.
Multi-Stream Extension of Variational Bayesian HMM Clustering (MS-VBx) for Combined End-to-End and Vector Clustering-based Diarization
Combining end-to-end neural speaker diarization (EEND) with vector clustering (VC), known as EEND-VC, has gained interest for leveraging the strengths of both methods.
Advances in integration of end-to-end neural and clustering-based diarization for real conversational speech
This paper is to (1) report recent advances we made to this framework, including newly introduced robust constrained clustering algorithms, and (2) experimentally show that the method can now significantly outperform competitive diarization methods such as Encoder-Decoder Attractor (EDA)-EEND, on CALLHOME data which comprises real conversational speech data including overlapped speech and an arbitrary number of speakers.
Integrating end-to-end neural and clustering-based diarization: Getting the best of both worlds
In this paper, we propose a simple but effective hybrid diarization framework that works with overlapped speech and for long recordings containing an arbitrary number of speakers.
Improving speaker discrimination of target speech extraction with time-domain SpeakerBeam
First, we propose a time-domain implementation of SpeakerBeam similar to that proposed for a time-domain audio separation network (TasNet), which has achieved state-of-the-art performance for speech separation.
