The STC System for the CHiME-6 Challenge
By Ivan Medennikov, M. Korenevsky, Tatiana Prisyach, Yuri Y. Khokhlov, Mariya Korenevskaya, Ivan Sorokin, Tatiana Timofeeva, Anton Mitrofanov, Andrei Andrusenko, Ivan Podluzhny and 2 more. 2020
60 citations recorded by the source corpus.
This paper is a description of the Speech Technology Center (STC) systems for the CHiME-6 challenge aimed at multi-microphone multi-speaker speech recognition and diarization in a dinner party scenario. We participated in both Track 1 and Track 2 and submitted our results for Ranking A as well as Ranking B for each track. The soft-activity based Guided Source Separation (GSS) as a front-end and a combination of advanced acoustic modeling techniques such as GSS-based training data augmentation, multi-stride and multi-stream self-attention layers, statistics layer and SpecAugment, as well as the lattice-level fusion of acoustic models were applied in the 1st track system. Our system for Track 1 was in the top three systems, achieving 30% relative WER reduction over the baseline. Additionally, lattice rescoring with a neural language model was applied for Ranking B. Overall, this led to 34% relative WER reduction over the baseline in Track 1. For Track 2, we proposed a novel Target-Speaker Voice Activity Detection (TS-VAD) approach to solve the diarization problem. Good diarization results made it possible to perform GSS on the obtained segments. TS-VAD is based on i-vector speaker embeddings, which are initially estimated using a strong diarization system based on spectral clustering of x-vectors. The back-end from the Track 1 system was used in the second track. The system for Track 2 demonstrated state-of-the-art performance, outperforming the baseline by 39% DER, 45% JER, 43% WER (Ranking A) and 45% WER (Ranking B) relative.
Subfields: Computer Networks and Communications
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Citations in the corpus
- SpeechStew: Simply Mix All Available Speech Recognition Data to Train One Large Neural Network (2021, neutral): “For example, the best previously published end-to-end model achieved 49.0% WER on the CHiME-6 dev set [8], while the best HMM model achieves 36.9% WER [9].”
- Towards a Competitive End-to-End Speech Recognition for CHiME-6 Dinner Party Transcription (2020, methodology): “To demonstrate a gap between our system and one of the best hybrid systems known to us at the moment, we also placed in the comparison the best single model from the STC CHiME-6 system [25].”