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Found 6 papers, 1 papers with code
Discrete Audio Representation as an Alternative to Mel-Spectrograms for Speaker and Speech Recognition
Discrete audio representation, aka audio tokenization, has seen renewed interest driven by its potential to facilitate the application of text language modeling approaches in audio domain.
Investigating End-to-End ASR Architectures for Long Form Audio Transcription
This paper presents an overview and evaluation of some of the end-to-end ASR models on long-form audios.
Automatic Speech Recognition
Automatic Speech Recognition (ASR)
+1
Fast Conformer with Linearly Scalable Attention for Efficient Speech Recognition
Conformer-based models have become the dominant end-to-end architecture for speech processing tasks.
A Compact End-to-End Model with Local and Global Context for Spoken Language Identification
We introduce TitaNet-LID, a compact end-to-end neural network for Spoken Language Identification (LID) that is based on the ContextNet architecture.
Multi-scale Speaker Diarization with Dynamic Scale Weighting
First, we use multi-scale clustering as an initialization to estimate the number of speakers and obtain the average speaker representation vector for each speaker and each scale.
TitaNet: Neural Model for speaker representation with 1D Depth-wise separable convolutions and global context
In this paper, we propose TitaNet, a novel neural network architecture for extracting speaker representations.
Ranked #1 on
Speaker Diarization
on CH109
