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End-to-end neural speaker diarization with self-attention
Speaker diarization has been mainly developed based on the clustering of speaker
embeddings. However, the clustering-based approach has two major problems; ie,(i) it is not …
embeddings. However, the clustering-based approach has two major problems; ie,(i) it is not …
Guided source separation meets a strong ASR backend: Hitachi/Paderborn University joint investigation for dinner party ASR
In this paper, we present Hitachi and Paderborn University's joint effort for automatic speech
recognition (ASR) in a dinner party scenario. The main challenges of ASR systems for …
recognition (ASR) in a dinner party scenario. The main challenges of ASR systems for …
Online end-to-end neural diarization with speaker-tracing buffer
This paper proposes a novel online speaker diarization algorithm based on a fully
supervised self-attention mechanism (SA-EEND). Online diarization inherently presents a …
supervised self-attention mechanism (SA-EEND). Online diarization inherently presents a …
Investigation of end-to-end speaker-attributed ASR for continuous multi-talker recordings
Recently, an end-to-end (E2E) speaker-attributed automatic speech recognition (SA-ASR)
model was proposed as a joint model of speaker counting, speech recognition and speaker …
model was proposed as a joint model of speaker counting, speech recognition and speaker …
BW-EDA-EEND: Streaming end-to-end neural speaker diarization for a variable number of speakers
We present a novel online end-to-end neural diarization system, BW-EDA-EEND, that
processes data incrementally for a variable number of speakers. The system is based on the …
processes data incrementally for a variable number of speakers. The system is based on the …
End-to-end neural diarization: Reformulating speaker diarization as simple multi-label classification
The most common approach to speaker diarization is clustering of speaker embeddings.
However, the clustering-based approach has a number of problems; ie,(i) it is not optimized …
However, the clustering-based approach has a number of problems; ie,(i) it is not optimized …