3 citations · 4 across the 7 of their papers we have counts for
7 papers
Elevating Robust Multi-Talker ASR by Decoupling Speaker Separation and Speech Recognition
Yufeng Yang, Hassan Taherian, Vahid Ahmadi Kalkhorani +1
Despite the tremendous success of automatic speech recognition (ASR) with the introduction of deep learning, its performance is still unsatisfactory in many real-world multi-talker…
Multi-channel Conversational Speaker Separation via Neural Diarization
Hassan Taherian, DeLiang Wang
When dealing with overlapped speech, the performance of automatic speech recognition (ASR) systems substantially degrades as they are designed for single-talker speech. To enhance…
Multi-resolution location-based training for multi-channel continuous speech separation
Hassan Taherian, DeLiang Wang
The performance of automatic speech recognition (ASR) systems severely degrades when multi-talker speech overlap occurs. In meeting environments, speech separation is typically per…
Breaking the trade-off in personalized speech enhancement with cross-task knowledge distillation
Hassan Taherian, Sefik Emre Eskimez, Takuya Yoshioka
Personalized speech enhancement (PSE) models achieve promising results compared with unconditional speech enhancement models due to their ability to remove interfering speech in ad…
One model to enhance them all: array geometry agnostic multi-channel personalized speech enhancement
Hassan Taherian, Sefik Emre Eskimez, Takuya Yoshioka +3
With the recent surge of video conferencing tools usage, providing high-quality speech signals and accurate captions have become essential to conduct day-to-day business or connect…
Location-based training for multi-channel talker-independent speaker separation
Hassan Taherian, Ke Tan, DeLiang Wang
Permutation-invariant training (PIT) is a dominant approach for addressing the permutation ambiguity problem in talker-independent speaker separation. Leveraging spatial informatio…
End-to-end attention-based distant speech recognition with Highway LSTM
Hassan Taherian
End-to-end attention-based models have been shown to be competitive alternatives to conventional DNN-HMM models in the Speech Recognition Systems. In this paper, we extend existing…